Microscopy imaging method and system

ABSTRACT

Linear fiducials including notches or chevrons with known angles relative to each other are formed such that each branch of a chevron appears in a cross-sectional face of the sample as a distinct structure. Therefore, when imaging the cross-section face during the cross-sectioning operation, the distance between the identified structures allows unique identification of the position of the cross-section plane along the Z axis. Then a direct measurement of the actual position of each slice can be calculated, allowing for dynamic repositioning to account for drift in the plane of the sample and also dynamic adjustment of the forward advancement rate of the FIB to account for variations in the sample, microscope, microscope environment, etc. that contributes to drift. An additional result of this approach is the ability to dynamically calculate the actual thickness of each acquired slice as it is acquired.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/783,054, filed Oct. 13, 2017, which is a continuation of U.S.application Ser. No. 15/420,844, filed on Jan. 31, 2017, now issued asU.S. Pat. No. 9,812,290 on Nov.7, 2017, which is a continuation of U.S.application Ser. No. 14/117,256, filed on Nov.12, 2013, now issued asU.S. Pat. No. 9,633,819 on Apr. 25, 2017, which claims the benefit ofpriority of U.S. Provisional Patent Application No. 61/485,713 filed May13, 2011, which are incorporated herein by reference.

FIELD

The present disclosure relates generally to charged particle beam (CPB)systems. More particularly, the present disclosure relates to imaging amaterial surface using a rastered beam system.

BACKGROUND

Examples of CPB systems include Scanning Electron Microscope (SEM)systems, Focused Ion Beam (FIB) systems and hybrids that include bothCPB types, which are commonly known as “Dual Beam” or “Cross Beam”microscope systems. A Focused Ion Beam system is commonly referred to asa FIB. FIB systems produce a narrow, focused beam of charged particles,and scan this beam across a specimen in a raster fashion, similar to acathode ray tube. Unlike the SEM, whose charged particles are negativelycharged electrons, FIB systems use charged atoms, hereinafter referredto as ions, to produce their beams. These ions are, in general,positively charged. Note also that CPB systems may include multiple ionbeams or multiple electron beams, perhaps in combination with eachother.

These ion beams, when directed onto a sample, will eject chargedparticles, which include secondary electrons, secondary ions (i⁺ or i⁻),and neutral molecules and atoms from the exposed surface of the sample.By moving the beam across the sample and controlling various beamparameters such as beam current, spot size, pixel spacing, and dwelltime, the FIB can be operated as an “atomic scale milling machine,” forselectively removing, or sputtering, materials wherever the beam isplaced. The dose, or amount of ions striking the sample surface, isgenerally a function of the beam current, duration of scan, and the areascanned. The ejected particles can be sensed by detectors, and then bycorrelating this sensed data with the known beam position as theincident beam interacts with the sample, an image can be produced anddisplayed for the operator. The imaging capability of FIB systems, andof similar CPB systems, is advantageous for many applications where itis necessary or beneficial to analyze structures or features having nanoscale sizes.

FIG. 1 is a schematic of a typical CPB system 10. This CPB system 10,also referred to as a dual beam or cross beam system, includes avertically mounted SEM column and a focused ion beam (FIB) columnmounted at an angle from vertical (although alternate geometricconfigurations also exist). A scanning electron microscope 41, alongwith power supply and control unit 45, is provided with the dual beamsystem 10. An electron beam 43 is emitted from a cathode 52 by applyingvoltage between cathode 52 and an anode 54. Electron beam 43 is focusedto a fine spot by means of a condensing lens 56 and an objective lens58. Electron beam 43 is scanned two-dimensionally on the sample by meansof a deflection coil 60. Operation of condensing lens 56, objective lens58, and deflection coil 60 is controlled by power supply and controlunit 45. Electron beam 43 can be focused onto sample 22, which is onmovable stage 25 within lower chamber 26. When the electrons in theelectron beam strike sample 22, various types of electrons are emitted.These electrons may be detected by various detectors within the electroncolumn or they may detected by one or more electron detectors 40external to the column.

Dual beam system 10 also includes focused ion beam (FIB) system 11 whichcomprises an evacuated chamber having an upper neck portion 12 withinwhich are located an ion source 14 and a focusing column 16 includingextractor electrodes and an electrostatic optical system. The axis offocusing column 16 is tilted at an angle, such as 54 degrees from theaxis of the electron column by example. The ion column 12 includes anion source 14, an extraction electrode 15, a focusing element 17,deflection elements 20, and a focused ion beam 18. Ion beam 18 passesfrom ion source 14 through column 16 and between electrostaticdeflection means schematically indicated at 20 toward sample 22, whichcomprises, for example, a semiconductor device positioned on movablestage 25 within lower chamber 26.

Stage 25 can preferably move in a horizontal plane (X and Y axes) andvertically (Z axis). Stage 25 can be tilted and rotated about the Zaxis. A door or load lock 61 is opened for inserting sample 22 onto X-Ystage 25 and also for servicing an internal gas supply reservoir, if oneis used. The door is interlocked so that cannot be opened if the systemis under vacuum.

An ion pump 28 is employed for evacuating neck portion 12. The chamber26 is evacuated with turbomolecular and mechanical pumping system 30under the control of vacuum controller 32. The vacuum system provideswithin chamber 26 a vacuum of between approximately 1×10-7 Torr and5×10-4 Torr. If performing gas assisted processes such as etching ordeposition, an etch retarding gas, or a deposition precursor gas isused, the chamber background pressure may rise, typically to about1×10-5 Torr.

The high voltage power supply provides an appropriate accelerationvoltage to electrodes in ion beam focusing column 16 for energizing andfocusing ion beam 18. When it strikes sample 22, material is sputtered,that is physically ejected, from the sample. Alternatively, ion beam 18can decompose a precursor gas to deposit a material on the surface ofthe sample.

High voltage power supply 34 is connected to liquid metal ion source 14as well as to appropriate electrodes in ion beam focusing column 16 forforming an approximately 1 keV to 60 keV ion beam 18 and directing thesame toward a sample. Deflection controller and amplifier 36, operatedin accordance with a prescribed pattern provided by pattern generator38, is coupled to deflection plates 20 whereby ion beam 18 may becontrolled manually or automatically to trace out a correspondingpattern on the upper surface of sample 22. The liquid metal ion source14 typically provides a metal ion beam of gallium. The source typicallyis capable of being focused into a sub one-tenth micrometer wide beam atsample 22 for either modifying the sample 22 by ion milling, enhancedetch, material deposition, or for the purpose of imaging the sample 22.Note that newer source technologies such as plasma, gas field ionsources and/or atomic level ion sources will produce other ionic speciesbesides gallium.

A charged particle detector 240 used for detecting secondary ion orelectron emission is connected to a video circuit 42 that supplies drivesignals to video monitor 44 and receiving deflection signals fromcontroller 19. The location of charged particle detector 40 within lowerchamber 26 can vary in different configurations. For example, a chargedparticle detector 40 can be coaxial with the ion beam and include a holefor allowing the ion beam to pass. In other configurations, secondaryparticles can be collected through a final lens and then diverted offaxis for collection.

A micromanipulator 47 can precisely move objects within the vacuumchamber. Micromanipulator 47 may include precision electric motors 48positioned outside the vacuum chamber to provide X, Y, Z, and thetacontrol of a portion 49 positioned within the vacuum chamber. Themicromanipulator 47 can be fitted with different end effectors formanipulating small objects.

A gas delivery system 46 extends into lower chamber 26 for introducingand directing a gaseous vapor toward sample 22. For example, xenondifluoride can be delivered to enhance etching, or a metal organiccompound can be delivered to deposit a metal.

A system controller 19 controls the operations of the various parts ofdual beam system 10. Through system controller 19, an operator cancontrol ion beam 18 or electron beam 43 to be scanned in a desiredmanner through commands entered into a conventional user interface (notshown).

In recent years, two and three dimensional imaging of large areas andvolumes in a charged particle beam system such as SEM, FIB, or SEM/FIBcombination microscope has attracted significant interest. Commercialsystems such as the Carl Zeiss ATLAS two dimensional imaging systemalong with three dimensional imaging systems such as the FEI Company“Slice and View” along with methods described in U.S. Pat. No. 7,312,448B2 have been available commercially. These techniques are generallyperformed on “bulk” samples, where the charge particle beam penetratesbut does not transmit through the sample. It should be noted that thisis quite different from the technique of electron tomography, whichrelies on the charged particle beam passing through the sample intransmission. While electron tomography is a well established techniquein transmission electron microscopy, and can yield three dimensionaldatasets, these datasets are limited in scale due to the necessity ofpassing the electron beam completely through the sample and detecting iton the other side.

The aforementioned “ATLAS” two-dimensional and “Slice and View” stylethree dimensional techniques are sophisticated in their own right,however they both approach the problem of acquiring large datasets in asimilar “step and repeat” fashion. In both cases a two-dimensional areais imaged either as a single image or as a collection of image “tiles”that may be “stitched” together to form a larger mosaic. Two-dimensionaltechniques tend to perform this step and repeat imaging over much largerareas than three-dimensional techniques, however three-dimensionaltechniques also remove a thin “slice” of material, then repeat theimaging process so as to build up a three dimensional dataset. Thisslice of material may be removed in several ways known in the art,including the use of a focused ion beam (typically at glancing angle,but occasionally closer to normal incidence), a broader ion beam whichis often combined with some sort of mechanical beam stop to create asharp edge, or an in-microscope ultramicrotome whose knife cuts awayeach slice.

CPB systems, such as FIBs or SEMs, have been used prevalently in thepast for imaging small regions of a sample at high resolution. In thefield of semiconductor circuits for example, typical structures beingimaged include transistor devices and other small structures havingdimensions from a few nanometers up to a few microns. In recent years,bio-medical applications are emerging in which higher resolution imagesfor a large area of a sample are desired using the aforementioned 2D and3D imaging techniques, and combining FIB and SEM. For example, imagingof a tissue sample having an area of 100×100 microns may be required inorder to facilitate visual identification of a particular structure ofinterest which may be present. Accordingly, a high resolution image ofthe entire area is required, otherwise it may not be possible tovisually identify the structure of interest. Furthermore, the particularstructure of interest may lie within a plane different from the exposedarea being imaged. In this example, if the imaged area of the sample isdefined by an x-y plane, then the tissue sample has a depth component,z. Therefore sections of the tissue sample are taken at predetermineddepths and the newly exposed area is imaged.

The problem with currently known techniques is the large amount of timerequired to image large volume samples at high resolution. Theincreasing demand for 3D high resolution images of 100 μm×100 μm×100 μmvolume samples is problematic. Typically sections are prepared ˜15 μmSections@˜3 nm pixels with ˜9 nm depth per slice using a FIB; moretypically three times this depth per slice is all that can be achievedusing other sectioning methods which can be used. Typical dwell timesfor the electron beam are on the order of 1 μs per pixel in order toobtain sufficient signal to noise. At 3 nm voxels with a dwell pointtime of 1 μs, 20 minutes of imaging time alone per section are required,and about 110 hours per μm of depth sectioned, which must be multipliedby 100 to section through 100 um of depth, and this is imaging timealone, i.e. it is assumed sectioning occurs concurrently or nearinstantly. Therefore a total of about 1.5 years of time is required toimage a 1,000,000 μm³ cube, assuming that the CPB system is capable ofoperating for this continuous period of time without malfunction orinterruption, or the sample undergoing sectioning can be reacquired andrealigned in an acceptable manner. Another issue related to imaginglarge areas is the fact that the sample is vulnerable to “drifting”during the imaging process, in which the sample moves due to mechanicalvariations in the stage supporting the sample, and/or thermal effects onthe environment of the microscope.

It is, therefore, desirable to provide a method and system for reducingthe amount of time required for CPB imaging while maintaining accuracy.

SUMMARY

It is an object of the present disclosure to obviate or mitigate at eastone disadvantage of previous CPB systems.

In a first aspect, there is provided a selective high resolution imagingmethod for a charged particle beam apparatus. The method includesacquiring and displaying a sample area image of a sample at a firstresolution; scanning at least one exact region of interest in the samplearea image; and acquiring and displaying an image of the at least oneexact region of interest at a second resolution greater than the firstresolution.

According to the embodiments of the present aspect, the sample can becross-sectioned to expose a new surface, of which the same exact regionof interest is imaged at the second resolution. This sequence ofsectioning and imaging the exact region of interest can continue until anew sample area image of the sample at the first resolution isrequested. At this time, new exact regions of interest can be added, orthe previous exact region of interest can be modified.

In yet further embodiments, alignment vernier notches can be formed onthe sample, which are visible as a pair of objects in cross-section thatapproach each other in distance as further cross-sections of the sampleare taken. Comparisons of the notch distances from a current to previouscross-section can be used to determine the exact cross-sectionthickness, for the purposes of adjusting a milling rate of the FIB.

According to further embodiments, any beam of the charged particle beamapparatus can be controlled with accuracy by use of a multi-digital toanalog circuit, which receives a primary digital code corresponding to aprimary deflection voltage for moving a position of the focused ionbeam, and at least one additional digital code proximate to the primarydigital code for generating a secondary deflection voltage. Thesedeflection voltages are averaged to provide a final deflection voltage.

Other aspects and features of the present disclosure will becomeapparent to those ordinarily skilled in the art upon review of thefollowing description of specific embodiments in conjunction with theaccompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present disclosure will now be described, by way ofexample only, with reference to the attached Figures.

FIG. 1 is a schematic of a known CPB system;

FIG. 2 is a schematic of a CPB system and a CPB workstation, accordingto an embodiment of the present invention;

FIG. 3 is a flow chart of a selective high resolution imaging method,according to an embodiment of the present invention;

FIG. 4 is an example extreme field of view image generated by the CPBworkstation of the present embodiments;

FIG. 5 is an example extreme field of view image with high resolutionimages of selected exact regions of interest generated by the CPBworkstation of the present embodiments;

FIG. 6 is an example extreme field of view image with overlayed highresolution images of the selected exact regions of interest generated bythe CPB workstation of the present embodiments;

FIG. 7 is a flow chart of a selective high resolution imaging method for3D imaging applications, according to a present embodiment;

FIG. 8 is an illustration showing a sequence of images obtained for eachsection of a sample, according to the method of FIG. 7;

FIG. 9 is a diagram illustrating a mosaic of a series of hexagonal tilesto achieve a best fit within a desired exact region of interest;

FIG. 10 is a flow chart of a method of acquiring an image from a sampleusing hexagonal tiles, according to a present embodiment;

FIG. 11 is a flow chart of a method for accurate movement of amicroscope stage, according to a present embodiment;

FIGS. 12A, 12B and 12C are example images of a region of interest usedfor the method of FIG. 11;

FIG. 13 is a block diagram of a scan generator configured to havemicroscope compensation features for a CPB system, according to apresent embodiment;

FIG. 14 is a flowchart of a drift compensation embodiment for improvingimaging of a large area, according to a present embodiment;

FIG. 15 is a flowchart of an imaging condition optimization method,according to a present embodiment;

FIG. 16 shows an example layout of images acquired by the method of FIG.15, according to a present embodiment;

FIG. 17 shows a top-down view of a sample having alignment notches,according to a present embodiment;

FIG. 18 shows the sample of FIG. 17 having a protective layer over thenotches, according to a present embodiment;

FIGS. 19A, 19B, 19C and 19D are example cross-section images a samplehaving the alignment notches;

FIGS. 20A, 20B, 20C and 20D are example cross-section images a samplehaving the alignment notches and parallel alignment notches;

FIG. 21 is a perspective view image of a sample after removal of severalsections;

FIG. 22 is an illustration of the mathematical relationship between thealignment notches and depth of section;

FIG. 23 is a flow chart of method for controlling an FIB sectioningrate, according to a present embodiment;

FIG. 24 is an example graph showing alignment data when using the 3Dtracking method embodiment of FIG. 23;

FIG. 25 is a block diagram of a proportional-integral-derivative (PID)controller;

FIG. 26A is a flow chart showing a probabilistic patterning method,according to a present embodiment;

FIGS. 26B, 26C and 26D are example diagrams of image drift;

FIGS. 27 and 28 are diagrams of scan areas;

FIGS. 29A and 29B is a flow chart of a scanning method, according to apresent embodiment;

FIGS. 30 and 31 are diagrams of scan areas after using the method ofFIGS. 29A and 29B;

FIG. 32 is a flow chart of a spatial super sampling method, according toa present embodiment;

FIG. 33 is a partial block diagram of a beam column;

FIG. 34 is a DNL plot for a commercially available DAC;

FIG. 35 is a block diagram of a multi-DAC voltage generator, accordingto a present embodiment;

FIG. 36 is a circuit schematic of the voltage averager shown in FIG. 35;and,

FIG. 37 is a DNL plot for the multi-DAC voltage generator of FIG. 35.

DETAILED DESCRIPTION

Generally, the present disclosure provides a method and system forimproving imaging efficiency for CPB systems while maintaining orimproving imaging accuracy over prior CPB systems. According to thepresent embodiments, a large field of view image of a sample is acquiredat a low resolution and thus, at high speed. The low resolution level isselected to be sufficient for an operator to visually identifystructures or areas of interest on the low resolution image. Theoperator (or an image analysis subsystem, which will be consideredanother type of “operator”) can select one or more small areas ofarbitrary shape and size on the low resolution image, referred to as anexact region of interest (XROI). The outline of the XROI is mapped to anx-y coordinate system of the image, and the CPB system is thencontrolled to acquire a high resolution image of only the XROIidentified on the low resolution image. For 3D imaging, once the XROI isidentified, each section of the sample can be iteratively imaged in thepreviously described manner, with the operator having the option toredefine the XROI later. The operator may also observe the informationcontained in the XROI image data and redefine the XROI based on thisinformation.

FIG. 2 is a block diagram showing the general relationship between thesame CPB system 10 shown in FIG. 1 and a CPB workstation 100, accordingto an embodiment of the present invention. CPB workstation 100 can be aseparate system that runs its own image and data processing algorithmsindependently of CPB system 10. More importantly, CPB workstation 100 isconfigured to control various aspects of CPB system 10. Alternately,control, image and data processing systems can be integrated into thehost CPB system. CPB workstation 100 can be used to “retro-fit” olderCPB systems while the functionality of CPB workstation 100 can beintegrated during assembly/design of newer CPB systems. According to thepresent embodiments, the CPB system 10 is a dual beam FIB-SEM system,which uses the FIB to mill the sample and the SEM to image the sample.For ease of reference, the term CPB system should be understood toinclude the aforementioned dual beam FIB-SEM system. The presentlydescribed embodiments are applicable to CPB systems in which a singlecolumn does both imaging and sectioning of the sample.

Many CPB systems have an accessible console with removable boardsinserted therein to control particular functions of the CPB system 10.Preferably, CPB workstation 100 includes a microprocessor, memory andmass storage, typically embodied as a computer workstation with amonitor, and a CPB system hardware interface 102 which can be connectedto the system controller 19 of the CPB system 10. In the presently shownembodiment of FIG. 4, a cable is the means for communicating the data,including but not limited to image data, to the workstation. However,any transmission means can be used, including directly or indirectlysupplying analog deflection voltages to control the position(s) of thebeam(s). The CPB workstation 100 is an active system which can controloperational functions of the CPB system, such as the pattern generator38, deflection controller 36 and the stage 25, while receiving videodata from video circuit 42 and any other. In general, CPB workstation100 can respond to data from the CPB system 10 and execute operations tocontrol the CPB system 10 in return.

The presently described embodiments are executed by the CPB workstation100 for controlling the CPB system 10 components to improve imagingthroughput while maintaining image quality. In a first embodiment,referred to as a selective high resolution imaging method, only specificregions of interest on a 2D large field of view image of a sample areacquired by the CPB system 10. Therefore, significant time savings areobtained because the entire large field of view is not imaged at highresolution. In the present embodiments, any number of specific regionsof interest, referred to as exact regions of interest (XROI) can beinputted to the CPB workstation 100 for acquiring high resolution imagesthereof, or a series of image resolution levels from low to highresolution.

FIG. 3 is a flow chart outlining an embodiment of the selective highresolution imaging method, which is described with reference to theexample images shown in FIG. 4 and FIG. 5. It is assumed that theoperator has visually identified a large region of the sample for whichan image is desired from either the native visual terminal of the CPBsystem 10 or the visual terminal of the CPB workstation 100.Alternately, this information can be determined by an image analysissubsystem using information obtained from correlative techniques such asoptical microscopy, coupled with an alignment phases wherein the datafrom these correlative techniques is mapped spatially into the frame ofreference of the CPB system, potentially defining XROIs in (X,Y,Z)coordinates in advance of any sectioning and imaging being performed. Atstep 200, the operator selects a large area of the sample to image, bycreating an outline of a region of desired size and shape on the visualterminal. The CPB system 10 is then controlled to acquire a lowresolution image of the selected area. This is generally done bycontrolling the electron beam to raster over the area. If the area istoo large, then the CPB workstation 100 will control the CPB system 10to acquire individual tiles of predetermined area and shape, andsubsequently mosaic them together to generate the final image. The stagemay need to be moved to a different position for acquiring an image forthe next adjacent tile. This can be done under control of the CPBworkstation 100. This is referred to as an extreme field of view (XFOV)image, shown in FIG. 4 as image 202 by example. The low level resolutionis selectable by the operator based on the level of detail deemedsufficient to identify particular structures or areas of interest forfurther analysis. For example, the resolution can be set to 30 nmpixels, which is relatively fast to image, and far improved relative tothe prior art technique of acquiring the entire XFOV image at the highresolution of a 3 nm pixel size.

This low resolution XFOV image 202 is displayed for the operator, whocan analyze the image and determine the presence of one or moredesirable XROI. At 204, the operator can create an outline of anyarbitrary shape on image 202, indicating a desired XROI. This is shownin FIG. 4 as XROI outlines 206, which can be applied using the interfaceof CPB workstation 100. It can be seen from FIG. 4 that the XROIoutlines 206 can have rectangular, square and round-type outlines.Because the XFOV image 202 is calibrated to an x-y coordinate system,the location of XROI outlines 206 are thus known. At 208, the CPBworkstation 100 receives and processes the XROI outlines 206, for thepurposes of configuring the position of the outlines and to generatecorresponding raster patterns for directing the beam rastering to beconfined within the XROI outlines 206 only. This may involve moving thestage to properly position the sample for rastering the XROI. At thispoint in time, the operator can optionally select a desired higherresolution level for imaging the areas within XROI outlines 206. At 210,the beam is controlled according to the raster patterns and the setresolution, and higher resolution images corresponding to the XROIoutlines 206 are generated and displayed. FIG. 5 shows the resultinghigher resolution images 212 corresponding to XROI outlines 206 of thelow resolution image 202, generated by the CPB workstation 100. Ifdesired, the operator can repeat the process by selecting a further XROIwithin the images 212 with an even higher allowable resolution.According to one embodiment, the higher resolution images 121 can bedisplayed within their own windows of the video monitor of CPBworkstation 100, thereby allowing the operator to zoom in and pan fordetailed visual inspection of the image. Note that the term rastering isused for convenience to describe the process by which the beam isscanned across the sample, however it is understood than many differentscanning strategies may be employed to obtain the optimal image qualityand speed, and these scanning strategies are collectively termed“rastering” for convenience.

To enhance the context of the higher resolution images 212, the CPBworkstation 100 can overlay the higher resolution images 212 over thelow resolution XFOV image 202 so that the operator can generally viewthe structures surrounding the higher resolution image areas. An exampleof this is shown in FIG. 6, where a high resolution image 214 has beenoverlayed onto the position of an XROI outline 216 of low resolutionimage 218. Once again, because the x-y coordinates of the XROI outlines206 are known relative to the XFOV image 202, the high resolution image214 can be positioned within outline 216 via image processing by CPBworkstation 100.

Accordingly, it is not necessary to image the entire XFOV image 202 athigh resolution, as this would consume a significant amount of time whenthe area of interest may only occupy a small portion of the image.Therefore, the combination of acquiring a low resolution image of thelarge XFOV area at high speed followed by selective high resolutionimage acquisition of smaller XROI areas improves can significantlyimprove the overall imaging throughput of the XROI at high resolution,relative to the prior art schemes. The final images can be subjected tographical post-processing, such as for example by adding virtualcolouring to features having the same particular grey-shadingintensities.

Another factor for improving overall imaging throughput is the abilityto select XROI outlines of shapes which can approximate the outline ofan area of interest. As shown in the previous example images, choosing aprecise XROI, rather than being limited to merely scanning a rectanglethat contains the region of interest, allows a reduction in the numberof pixels to be scanned, thus increasing throughput. Any otherimprovements to the system, such as signal to noise, detectorefficiency, beam current and spot size improvements, merely make theXROI approach more efficient. For example, a common yeast cell isapproximately spherical. It is known that the volume of a cube ofdiameter D is approximately twice the volume of a sphere of diameter D.Thus, if one is constrained to imaging a contant rectangular or squarearea, the CPB system requires approximately twice as long to imagerelative to imaging a circular XROI. Equations 1 and 2 belowmathematically illustrate this relationship.

V_(Cube)=D³  Equation (1)

V_(Sphere)=4/3πr³≅½³  Equation (2)

The previously discussed selective high resolution imaging methodthroughput benefits are significantly scaled when applied to generatingdata for 3D reconstruction of a sample. This is due to the fact that foreach new section of the sample, a new image of a region of interesttaken at high resolution is acquired. As previously discussed, the priorart technique of obtaining high resolution images of a large area XFOVfor a plurality of sections can be unacceptably long.

FIG. 7 is a flow chart outlining an alternate embodiment of theselective high resolution imaging method, adapted for 3D imagingapplications. It is noted that processes 300, 302, 304 and 306 are thesame as processes 200, 204, 208 and 210 of the method of FIG. 3. The 3Dselective high resolution imaging method of FIG. 7 is now discussed withreference to the diagram of FIG. 8. The diagram of FIG. 8 is anillustration of the sequence of images obtained for each section, orslice, of the sample. Starting at 200, a low resolution image of thesample area is acquired by the CPB system for display on a monitor ofthe CPB workstation 100. This image is referred to as a key frame image.In FIG. 8, this first key frame image 350 appears at the left-most side,and includes image information for visual display to the operator. Keyframe image 352 is an enlarged version of key frame image 350 to betterillustrate some displayed features. At 302, the operator will identifyan area of interest on the key frame image 352, and add XROI outline 354on the display. This information is received and processed by the CPBworkstation 354. At 304, the CPB workstation 100 configures the rasterpattern and position for the CPB system 10, and at 306, a highresolution image of only the area outlined by XROI outline 354 isdisplayed on the monitor of the CPB workstation 100. Imaging at 306 isexecuted by rastering the electron beam 356 within the XROI outline 354.As shown in FIG. 8, the high resolution image corresponding to XROIoutline 354 appearing on the monitor of CPB workstation 100 is frame358.

Now that the region of interest of the current section of the sample hasbeen imaged at high resolution, the method proceeds to 308 where a newsection of the sample is obtained. In FIG. 8, the FIB beam 360 whichrasters side to side in the x-axis, is advanced by a predetermineddistance or depth in the z-axis, ie. towards the right side of thediagram, and activated to cut away the sample material to expose a newsample surface. Note it is also possible that the FIB beam is advancedin smaller increments in a more continuous manner than in the discretesteps described above. Returning to FIG. 7, the method then proceeds to310 to determine if a new low resolution key frame image is required.This is typically determined by the operator who can control the CPBsystem 10 via controls of the CPB workstation 100 to request a new keyframe. If no key frame is required, the method returns to 306 foracquisition and display of a high resolution image of the same XROIoutline 354, for the new sample surface. Processes 306, 308 and 310 loopiteratively until the operator decides to terminate image acquisition,or until the operator determines that a new key frame image is required.This is due to the fact that during the iterations of 306, 308 and 310,the monitor displays only the acquired high resolution image defined byXROI outline area 354. Therefore, other structures surrounding this areacannot be seen.

In the present example of FIG. 8, after the fourth frame, the operatordecides at 310 that a new key frame image is required. The method thenreturns to 300 and the CPB system 10 acquires new key frame image 362 inFIG. 8, shown in enlarged format as key frame image 364. As previouslymentioned, this is done by scanning the entire sample area at lowresolution, which may involve iteratively imaging an area, moving thestage to image another area, and tiling the resulting images together.At 302, the operator can add or remove XROI outlines. In the presentexample, a new XROI outline 368 is added to the displayed image by theoperator, which is received and processed by the CPB workstation 100 at302. After processes 304 and 306 are executed by the CPB system 10, theresulting image frame 370 is displayed for the operator. As shown inFIG. 8, only high resolution images for original XROI outline 354 andfor new XROI outline 368 are shown in image frame 370. Then the systemiteratively sections and images these regions automatically.

By periodically acquiring “Key Frame” images of the entire sectionedsurface of interest, this allows the automatic or manual selection ofnew XROI's that may appear during sectioning, and the dynamic changingof the existing XROIs. Note that the image data from each (typicallyhigher resolution XROI) can also be interrogated to determine if, on thenext or future XROI imaging passes, the boundaries of the XROI should bemodified.

According to a further embodiment, of the present invention, overallimaging throughput can be increased by examining a particular region ofinterest and determining based on pixel intensity of a rapidly acquiredlower resolution image, whether an area should be scanned. For example,when imaging biological material stained using common protocols thatintroduce heavy metals into the tissue, and observing/detectingbackscattered electrons with inverted contrast (as is common), tissueappears dark (more signal, resulting in a dark pixel when observing withinverted contrast) and embedding material appears brighter (less signal,resulting in a bright pixel when observing with inverted contrast).These images can be processed (including Key Frames or XROIs) to detectregions containing only embedding material, and avoid imaging theseregions of embedding material, or avoid imaging regions of embeddingmaterial that are sufficiently distant from regions that are identifiedas not being embedding material (thus imaging regions of embeddingmaterial near what may be true sample), so as to increase throughput.

Alternately, it is possible to analyse the signal as it is beingaccumulated within a single pixel or dwell point, and determine during asubsampling time, such as the initial subsampling time, for that pixelwhether sufficient signal has been detected to expect that pixel is aregion of embedding material, and not a region of tissue. If it isdetermined that the pixel is tissue, the dwell continues to improvesignal to noise; if not, the beam is advanced to the next pixel beforethe full dwell time is reached, thereby improving throughput. The“advanced from” pixel (presumed to be embedding material) may have it'sintensity normalized as if the signal had been acquired for the fulldwell time, and it may also be flagged somehow to indicate that it wasnot dwelled for the complete period, and optionally how long a dwelltime did occur. Additionally, the dwell time required to achieve acertain number of counts can be recorded, advancing to the next pixelwhen a predetermined number of counts has occurred, and thus generatinga “dwell time to a given number of events” image map, rather than animage map of the intensity observed in a fixed dwell time.

According to another embodiment, the CPB system 100 can be configured toanalyse information from clusters of neighbouring pixels to determine ifan “advanced from” pixel should truly have been advanced from, or ifperhaps it's signal did not occur during the subsampling time do to someanomaly, based the known methods of Poisson and other discrete particlecounting statistics. Thus a particular pixel within the same image canbe revisted/re-imaged based on processing of neighbouring pixels shouldthose pixels appear to indicate a high probability that a particularpixel has been undersampled. In systems where slices are archived, aneighboring pixel can be within other slices, thus the other slices canbe re-imaged at a later time.

The “time of flight” between a charged particle leaving the CPB column,impacting the sample to generate a secondary signal, and this secondarysignal being detected can be significant enough that a considerablelatency can exist in the system, in which case an image can beaccumulated from multiple “subimage” passes over the same physicalregion, each pass taking sufficient time that the individual pixellatency is small in comparison to the time per pass to acquire asubimage. The exact scan strategy to create the next subimage may bemodified based on an analysis of pixel information in one or moresubimages, using the methods described above, ultimately building up afinal image potentially composed of pixels that have seen various totaldwell times, in a similar manner o the fashion described above, butovercoming the impact of time of flight latency or other latencies suchas detector response or dead time. Note also that image alignmenttechniques may be used between subimage passes to correct for sample orinstrument drift which would otherwise cause the system to performsubimaging passed in what is potentially the wrong location.

As the end data set of this serial sectioning and imaging methodultimately ends up with three dimensions of data, represented by a“stack” of images which may be aligned, there are cases where one caninfer from a pixel in stack N whether a pixel at stack N+1 requiresimaging. Thus, referring to an earlier example, knowing no tissue ispresent in a pixel in stack N, it may be sufficient not to botherimaging that same position in stack N+1 (or indeed up to N+M).Alternately, reconstruction algorithms may be able to interpolate orotherwise deduce the necessary data even from “sparse” images, forexample if only half of the pixels are imaged on even sections, theother half on odd slices (after the fashion of a checkerboard—“redsquares”=pixels imaged on even slices, “black squares”=pixels imaged onodd slices. Image processing at the acquisition level may be used tointerpolate or otherwise “fill in” the missing data to increasethroughput. Note that this is not limited to a “checkerboard” approach,and as the rate limiting step is often the SEM imaging, these “sparseimaging” approaches, be they temporal or geometric can lead to greaterthroughput which can be used to achieve finer slice resolution in theFIB/SEM system, which in turn may allow for more intelligent algorithmsto supply the intensity values for those pixels “skipped” using sparsemethods.

The previously described 2D and 3D imaging methods can benefit fromadditional improvements over control of the CPB system 10, which can beprovided by CPB workstation 100. These are referred to as multi-passrastering, spatial super-sampling and temporal sub-sampling, which canbe optionally enabled during the imaging phase in order to improve dataquality or optimize a particular component of the signal that is used togenerate the image.

While the previously described 3D imaging method is described for a dualbeam FIB-SEM system, the techniques can be applied to imaging methodswhere the slices of a sample are archived, such as by using the“ATLUM/ATUM” technique developed by Dr. Jeff Lichtman et al. of theDepartment of Molecular and Cellular Biology of Harvard University. Inthe Lichtman technique, slices of a sample are pre-prepared andsubsequently imaged. Therefore an operator can return to any slice forre-imaging any particular region of interest. The application of thepresent 3D imaging embodiments to archived slices includes performingimaging at multiple resolutions—a first, lower resolution pass through aseries of sections, which are then image processed to determine wherehigher resolution imaging is to occur, after which further, higherresolution images are acquired. The previously described imaging methodscan be applied to all stages of this process.

According to further embodiments, the previously described embodimentscan complement the technique of Lichtman, which may be used to acquiresections that are relatively thick—for example, 300 nm in thickness. Forexample, “lower resolution” imaging of each section and its XROIs (whichare not necessarily limited in area and may indeed be the entiresection) is executed by the CPB system 10. Further processing thendetermines regions that require higher resolution data acquisition. Atthis point, it is possible to use the FIB/SEM approach to slice throughthe “thick” section in a serial manner, for example, sectioning at 90degrees to the originally sectioned surface, and obtaining a higherresolution data set within this FIB/SEM sectioning area on each desiredthick section, once again applying the XROI technique as desired tofurther improve throughput.

During the process of imaging one or more XROI's, it is possible that agiven XROI or the agglomerate of XROI's desired may exceed the maximumsize of image unencumbered by differential non-linearity (DNL) artifactsthat is available using the digital to analog (DAC) hardware. This canbe dealt with using techniques such as the multi-DAC approach discussedlater, and/or through the use of a mosaic tiling approach, sometimesreferred to as montaging of an image. The current state of the artrequires the use of rectangular image tiles, however as discussed below,this can be improved in many cases using non-rectangular tiles.

In order to image a large area at high resolution, it is necessary toacquire a mosaic of multiple smaller images at high resolution andstitch them together. This is commonly done with square or rectangularimages as they are normally obtained from a scan generator. However, asthe size of the field of view becomes large for ultra-high pixel densityimages, there may be scan and beam distortions that limit the extent ofthe images. Examples of scan distortions include a “pin cushion” typedistortion where the normally straight edges of a square or rectangleappear deflated, and a “barrel” type distortion where the normallystraight edges of a square or rectangle appear inflated. Examples ofbeam distortions includes focus and astigmatism distortions. These typesof distortions are well known in the art.

Since these distortions typically have circular symmetry, the greatestdistortions occur in the corners of a square or rectangular image. Toavoid these artifacts yet still use the largest field of view possibleto minimize the number of images required, the CPB system 10 can beconfigured by the CPB workstation 100 to acquire images with a hexagonalshape that tile to completely fill the mosaic while allowing forefficient stitching. Therefore, overall image quality is improved overrectangular tiling, while improving image capture throughput. Themathematical reasoning behind using hexagonal shaped tiles is furtherexplained. FIG. 9 is a diagram illustrating a mosaic of a series ofhexagonal tiles to achieve a best fit within a desired XROI. As shown inFIG. 9 by example, a rectangular XROI 400 can be assembled frommosaicing multiple hexagonal tiles 402. For the hexagonal tiles 402 onthe periphery of XROI 400, where only a part of the tile is within XROI400, the tile scan patterns are truncated such that no scanning of thehexagonal tile area outside of XROI 400 is executed.

If it is assumed that the distortions become unacceptable a distance rfrom the centre of the field of view, then the area of the largesthexagon with acceptable distortions is 3√{square root over(3)}/2r²≈2.59r², compared to 2r² for a square image. This means 30%fewer images are required when using hexagons compared to squares, whichare the most efficient rectangles, thereby requiring fewer stagemovements and less stitching. For comparison, using a non-square imagewith an aspect ratio of α, the area is even smaller than for a squareimage (A=4r²/(α+1/α), 0≤A≤2r²). Any other combination of shapes thatcompletely fills the space can be used, such as octagons and diamonds byexample.

FIG. 10 is a flow chart illustrating a method of acquiring an image froma sample using the hex tiles. This method can be incorporated into thepreviously described methods of FIG. 3 and FIG. 7 where high resolutionimages of an XROI is acquired. The method starts at 410 when the XROIoutline is received by the CPB workstation, after the operator has drawnit on the low resolution key frame image by example. The CPB workstationthen executes an optimization algorithm to achieve a best fit mosaic ofhexagonal tiles that would encompass the XROI at 412. For example, theoptimization criteria could include ensuring that the maximum number offull hexagonal tiles are included with the XROI. At 412, imageacquisition begins and the CPB system 10 is controlled to acquire imagesat high resolution for each hexagonal tile by rastering the electronbeam over the sample surface within a virtual hexagonal boundary. Afterthe image data is acquired for one hexagonal tile area, the stage may bemoved to better position the beam for rastering of the next hexagonaltile area. Once all hexagonal tiles of the established hex tile patternhave been imaged, post processing operations are executed at 416 tomosaic the hexagonal tiles together. This resulting image is thendisplayed for the operator on the monitor of the CPB workstation 100.

Creating such a mosaic requires some translation from tile to tile, beit using a Pan/Shift capability of the microscope, or for largerdistances a physical motion of the microscope stage. In the case of aphysical motion, in either 2D or 3D image acquisition, a technique ofhigh resolution XROI imaging can be employed to improve the accuracy ofthe stage motion as follows.

In many instances, the stage should be moved by a very precise amountrelative to the current position. If the stage is not accurate enough,due to mechanical limitations for example, then such a move is notnormally possible to the required precision. Assume for example that onewants to locate a specific contact in a semiconductor memory array.Because of the repetitive nature of the array, it is only possible toidentify a contact based on its exact position relative to the corner ofa memory array which is unique enough to be identified. Using a stagemovement from the corner results in a given uncertainty because of theinaccuracy and imprecision of the stage. If the cell size is smallerthan the uncertainty of the stage move, then it is not possible toidentify which cell and which contact around the resulting approximateposition is the actual target. According to a present embodiment, thestage can be moved by the desired amount, followed by an identificationof precisely how much it has actually moved. Any additional correctionis then accomplished by precisely shifting the beam. This is achieved byusing high resolution imaging at a large field of view (FOV) and patternrecognition. FIG. 11 is a flow chart outlining a method for accuratemovement of a microscope stage.

The method of FIG. 11 begins at 450, by automatically or manually movingthe stage to a region of the sample containing a suitable feature to beused as a reference fiducial. At 452, a sufficiently high resolutionXROI image of the region of interest around a fiducial, large enough tocover a region greater than the aggregate of the fiducial size plus amultiple of the stage accuracy, for example the size of the fiducialplus twice the stage accuracy, is acquired. A resolution is consideredsufficiently high if, after a subsequent offset error value isdetermined as discussed below, the sub-pixel uncertainty on thatcalculated offset error value is sufficiently small as to give thedesired positional accuracy. The magnification is set such that thefield of view is at least twice the required stage movement in either orboth of x and y axes. Then the stage is moved by the desired amount at454. At 456, another high resolution XROI image is acquired of theregion of interest at the position on the sample where the fiducial isexpected to be, assuming exact stage positioning has been achieved. Thiscan be easily computed by the CPB workstation 100 by applyingmathematical translation techniques. The high resolution images from 454and 456 are compared to each other at 458 to determine the precise errorof the positioning of the stage, obtaining an (x,y) offset valuerepresenting the measured error in positioning. This offset value can becomputed by many methods well known to those skilled in the art,including cross-correlation based image comparison techniques. In otherwords, by comparing the two imaged regions of interest with each other,the second image could have the fiducial offset in position relative tothe fiducial in the first image.

FIG. 12A is an example high resolution image of a region of interest462, including a reference fiducial 464 in the form of a “+” symbol,acquired at step 452 of FIG. 11. FIG. 12B is an example high resolutionimage of the region of interest 462 acquired at the expected location ofthe reference fiducial 464. FIG. 12C illustrates the difference inposition between the first and second reference fiducial images, wherethe dashed line reference fiducial represents its original referenceposition. Now the beam can be precisely shifted by the error amount at462, resulting in the center of the field of view being precisely offsetrelative to the reference fiducial, allowing the user to zoom in foreven higher resolution imaging. Note that a combination of beamplacement and stage placement shifting can be performed, if need be inan iterative fashion, until the desired accuracy is achieved.

According to an alternate embodiment, the FOV can be only as large asthe required stage motion by doing two stage moves and reacquiring areference image between the two. Assuming a high resolution image at32768 pixels along the FOV and an absolute scan accuracy of 4 pixels,this method would result in a precision of 10 nm for stage movements upto 80 microns, or 25 nm precision is achievable for stage movements upto 200 microns. In any implementation, an improvement in scan accuracywill directly result in more precision or larger stage movement giventhe same precision requirements.

In another alternate embodiment, the method involves continuouslyscanning a region of interest at a frequency sufficient that any motionin that feature would be a small increment during a single image pass,and tracking that feature across a high resolution scan (this is greatlyenable by a high resolution of 32k×32k or higher existing technologybeing typically limited to 8k×8k) while the stage is moved to thedesired location—thus by relying on the well calibrated scan andtracking the feature continuously through high speed scanning, a precisedetermination of the position of the feature can be achieved, relativeto the accuracy of the scanned image field, throughout the entire periodduring which the stage is moving, which typically will yield greaterpositional accuracy than can be achieved by a mechanical stage system.

When acquiring images at large fields of view, it is common to encounterdistortions that affect the quality of the image. These may be dividedin to three general groups: scan distortions due to lensing or sampletilt effects, beam distortions such as astigmatism and focus differencesacross the image or sample induced distortions. According to presentembodiments, the CPB workstation 100 is configured to mitigate oreliminate some of these distortions through active and/or passiveprocesses. Active distortion mitigation methods include modifying thescan and beam conditions. Passive distortion mitigation methods includepost-processing of the images and some correction of these artifacts,Following is a non-exhaustive listing of possible distortions that couldbe corrected for with the presently described distortion mitigationembodiments: (i) loss of focus and stigmation, particularly in thecorners of the image; (ii) barrel or pincushion distortion at large FOV;(iii) leading edge distortion due to beam dynamics; (iv) tilt parallax(trapezoidal distortion); (v) leading edge distortions due to minorsample charging; and (vi) focus changes due to sample geometry.

One embodiment for large area distortion mitigation is dynamic scan andbeam compensation.

When dealing with geometric distortions, with the use of a digital scangenerator, it is possible to apply a correction to the scan such thatthe actual scan after distortion by the column and/or sample producesthe original desired result. Consider the microscope has a non-idealtransfer function T({right arrow over (r)}) that converts an input scanposition {right arrow over (r)}=(x,y) into a real position {right arrowover (r′)}32 (x′, y′):

{right arrow over (r′)}=T({right arrow over (r)})

If the inverse T⁻¹({right arrow over (r)}) of this transfer function isknown, then the desired scan position is obtained when a corrected input{right arrow over (r″)}=T⁻¹({right arrow over (r)})

T({right arrow over (r′)})=T(T ⁻¹({right arrow over (r)}))={right arrowover (r)}

Accordingly, the correction can be generated by two different techniquesaccording to the present embodiments. In one embodiment, the inverse ofthe transfer function is parametrized analytically and the parametersare adjusted until the proper output is obtained. In another embodiment,a calibration grid is used and a map of the inverse transfer function isbuilt numerically based on the measured discrepancy between the inputand the output. In either case, for a digital scan generator equippedwith a digital signal processor (DSP) or a field programmable gate array(FPGA), the corrected input can be calculated or accessed from a lookuptable and applied directly in the scan generator hardware. For simplecorrections such as a tilt parallax, the function can be implemented asan analytical function, but for more complicated corrections, apredetermined lookup map can be employed.

A second embodiment for large area distortion mitigation is dynamicfocus tracking along a cross-section.

In the situation when imaging a sample that is tilted and two faces areexposed to the beam (for example the original surface of the sample andthe cross-sectioning face), the focus of the beam is dynamicallyadjusted as a function of the position in the image in order to keep infocus along both faces of the sample. Current implementations of dynamicfocus are limited to allow tracking within one plane, either the surfaceor the cross-section. By having a more complex tracking routine, it ispossible to determine, based on the current scanning position in theimage, which plane is being scanned and thus adapt the focusappropriately. The focus is constantly adjusted according to the knownsample topography to preserve focus on the entire area, provided thefocus adjustment can be performed sufficiently fast to keep up with thescan. Under these conditions, the focus can be adjusted in order tomitigate defocussing effects away from the centre of the image byconstantly adjusting the focus according to a predefined map. If optimalstigmation varies within the image field of view, then it is alsopossible to adjust the stigmation according to a pre-established map,according to the position of the beam in the image.

Currently, calls to adjust the focus and stigmation are generated insoftware by the native CPB system 10, so changes to the focus andstigmation within a scan line are only possible for very slow scans.According to the present embodiment, the scan generator is configured tooutput not just the x and y deflection signals but also a focus andstigmation correction signal, which would make the system operationalfor regular scan speeds. In this embodiment, the scan generator isconfigured to include a lookup table in memory for both the focus andstigmation as a function of beam position. As the scan is generated, thefocus and stigmation outputs are converted to signals usable by themicroscope using a standard digital to analog conversion andamplification mechanism.

FIG. 13 is a block diagram showing a scan generator configured for boththe previously described dynamic scan and beam compensation, and thedynamic focus tracking compensation embodiments. The scan generator 500includes a processor 502, such as a DSP or an FPGA by example, thatreceives input scan information SCAN_IN and provides a corrected outputto a digital to analog converter (DAC) 504. The DAC 504 converts thereceived digital information into an analog signals collectivelyreferred to as A_OUT, which include x and y axis deflection voltagesignals for controlling beam position, and focus and stigmation controlvoltages. The processor 502 is configured to include the aforementioneda function processing circuit 506, and a lookup table (LUT) 508. For thepresent embodiment, the processor 502 can selectively determine if thereceived input scan information SCAN_IN should be processed by functionprocessing circuit 506 or LUT 508. The function processing circuit 506is used for readily modeled dynamic scan, focus, stigmation and beamcompensation. The LUT 508 is embodied as memory within the processor 502and is used for more complex dynamic scan, focus, stigmation and beamcompensation.

Either in combination with or independently from the dynamic beam andscan and focus tracking embodiments above, the images can bepost-processed to compensate for large area imaging distortion.

Assuming an image was acquired with a known distortion, post-processingoperations can be performed on the image to remove the effect of thedistortion. This is commonly done in optical imaging or photographywhere some lens artifacts are well defined. Wide angle lenses tend tohave some degree of barrel distortion which is commonly post-correctedinside the camera as the image is saved to a file. According to thepresent embodiment, the CPB workstation 100 is configured to include thesame type of process, where the acquisition engine automatically morphsthe image as it is acquired so that the output obtained by the operatoris free from distortions. As in the case of in-camera processing, thedistortion should be well established prior to acquisition.

One example implementation is for imaging moderately charging samples.When imaging a charging sample with an SEM, the act of scanning the beamresults in charge accumulation on the sample surface which slightlyaffects the beam position. Under certain conditions, the scanned area ofthe image will have a systematic compression on all lines, on the edgewhere the scanning begins. In this case a standard raster scan is used,imaging all lines from left to right. By comparing the resulting imageto the known geometry of the sample, a simple model of exponential decaycan be used to accurately model the amount of lateral shift of eachpixel from its nominal position,

${dx} = {A\mspace{11mu} e^{- \frac{x}{\tau}}}$

(where A is the shift of the left-most pixel and τ characterizes howfast the shift decays to 0). Using this model, the necessary warping ofthe measured image can be performed in real-time such that the operatoris shown a proper image free of the artifact.

Another contributor to large area imaging distortion is dynamic drift.Acquisition of a high pixel density image takes much longer than underconventional imaging conditions, and therefore the stability of themicroscope may result in unacceptable drift during the duration of theacquisition. The sources of this drift may be electronic (beamdeflection shift) or mechanical (stage drift). Typically, the largestsource of drift is stage drift, assuming the microscope and electronicsare up to stable operating conditions.

According to a dynamic drift compensation embodiment, this drift iscompensated for by shifting the beam systematically at the scan levelaccording to a predictive drift model, This model is developed fromprior images, where, for example, a given system has a known relaxationdrift after a stage move. Alternately, the drift model can be generateddynamically by regularly pausing the imaging and performing registrationon a fiducial to evaluate the current amount of drift. A model can thenbe applied to anticipate the amount of correction that is needed tocompensate for the drift.

As an example, a 32k×32k image of 1 Gigapixel is acquired using a dwelltime of 2 ps will take approximately 35 minutes. Assuming the systemstage drift specification is 3 nm/minute, the stage may have drifted bynearly 100 nm at the completion of the image. If the image was acquiredwith a resolution of 5 nm, this will result in an error of 20 pixelsbetween the top and the bottom of the image. By pausing the image anddetermining the amount of drift periodically, this error can be reduced.By way of example, by pausing every 5 minutes, it is possible to reducethe error to 15 nm (3 pixels), or less if the drift is systematic andcan be modeled properly.

According to the present embodiments, it is also possible to measure thechange in environmental variables such as for example, the temperature,sound, vibration or pressure, in close proximity to the sample or themicroscope, and by evaluating the drift as a function of suchenvironmental variables, create a predictive model for the impact achange in such a variable may have on the image (including drift). Forexample, it may be that a rise in temperature typically corresponds to acertain drift in a certain direction that lags the measured temperaturerise by a certain time. Thus it may be possible to dynamically adapt thescan to (optimally smoothly) compensate for this drift during the courseof acquisition of one or more images.

It is also possible to look for rapid changes in these variables, suchas the sound of a slamming door, and (either additionally or in placeof) also evaluate the local portion of the image as acquired (such asthe last few scan lines) against the average metrics of the image. Atpresent, this can be done by analyzing the entire image on completion.According to a present embodiment an algorithm can be developed toanalyze pairs of lines in the image for the purposes of calculating thefollowing:

-   -   The calculated threshold (product of the user defined threshold        by the standard deviation of the standard deviation of each line        pair difference)    -   The average standard deviation of each line pair difference    -   The standard deviation of the standard deviation of each line        pair difference    -   The largest calculated standard deviation for a given line pair        difference. This also includes how much larger than the average        it is and for which row this value was observed.    -   Determining a FAIL or PASS state, whether the largest calculated        standard deviation exceeds the threshold above the average or        not.

However, the current art requires completion of the image, andpost-calculation of the PASS or FAIL state, which can in turn triggerthe entire image to be re-acquired. It is advantageous to look forproblems with the image either by dynamically performing such analysisas the image is acquired, or by measuring environmental variables andlooking for signatures of events previously determined to cause problemsin the image. When a likely problem is discovered, for example a soundis detected by an acoustic monitoring circuit that is above thethreshold determined to be sufficient to cause a deficiency in the imageit is then possible to repeat a portion of the image to remove theproblem. For example, if a vibration is detected either by analyzing thelines of the image as discussed above, or by observing an environmentalevent (such as a sound spike caused by the slamming of a door), theacquisition can be stopped and the beam “backed up” to a point spatiallythat had been scanned prior to the event being detected, and this smallportion of the image can be re-scanned, rather than requiring the entireimage to be rescanned. This also has the advantage that the “back up andrepeat” happens temporally very close to the original scan, leavinglittle time for errors such as drift to occur. And additional step of“drift correction” to align the last “known good” portion of the imageprior to the environmental event and the first portion of the backed-upand re-imaged portion may be performed during acquisition to ensure thefinal image that is saved has a seamless transition across the portionof the sample that was being imaged when the original environmentalevent occurred.

FIG. 14 is a flowchart summarizing a general drift compensationembodiment for improving imaging of a large area. Starting at 520 adrift model, such as to model relaxation drift by example, is developedand if necessary updated for the CPB system 10 by either analyzing priorimages scanned by the system, or through periodic pausing of the imagingand performing registration on a fiducial to evaluate the current amountof drift. This model can also incorporate the effect of environmentalvariables as previously discussed. Scanning of the sample surface beginsat 522 with application of feature small to adjust the scan position ofthe beam to compensate for the expected drift of the sample. Duringscanning, the system is actively monitoring predetermined environmentalvariables which may affect instantaneous shifting of the sample. If anenvironmental event is detected at 524, just the portion of the imagethat was scanned prior to detection of the environmental event isrescanned. The method then returns to 522 to resume scanning of thesample. This is done by pausing the current scan, and repositioning thebeam to a point on the sample anytime before the environmental event wasdetected. Otherwise, in the absence of a detected environmental event,the system continues scanning the sample. In the present method, thedrift model developed at 520 can be updated periodically, either at apredetermined time or schedule, and/or when some static environmentalcondition has changed since the last drift model was developed.

In addition to automatically attempting to improve the imagingconditions of the microscope, the operator may wish to have more directcontrol over the imaging conditions.

When acquiring very large images, it is difficult to determine ifimaging conditions such as focus, stigmation, etc. are optimized for thebest trade-off across the entire image. It is therefore advantageous tobe able to have a single display that allows the operator to seemultiple regions of the large image at full, or otherwise high,resolution at essentially the same time. According to a present imagingcondition optimization embodiment, the operator can view multiple imagestaken at different areas of the sample simultaneously for the purposesof manually adjusting imaging conditions to obtain the optimal trade-offfor best results in all parts of the image. FIG. 15 is a flowchartoutlining the present imaging condition optimization method according toa present embodiment. It is assumed that the operator has selected aregion of interest of the sample for which an image is required. Animage of the top left corner of the ROI is acquired at 550, followed byacquisition of images of the other 3 corners of the ROI at 552, 554 and556. At 558, an image is acquired at a generally central area of theROI. Once these images have been acquired, they are simultaneouslydisplayed on the monitor of the CPB workstation 100 at step 560.Alternately, the display of each region made be dynamically updated asthe scan is performed. Now the operator has the ability to adjust andset the imaging conditions which are optimal for all 5 displayed regionsof the ROI. Full image acquisition of the ROI can then proceed at 562with the set imaging conditions.

FIG. 16 shows an example layout of all 5 images acquired in the methodof FIG. 15 displayed in a window 570 of the monitor of CPB workstation100. In the presently shown example, the positions of the imagesgenerally corresponds to the regions of the ROI they were taken from. InFIG. 16 by example, the top left corner image 572 displays the acquiredimage taken from the top left corner of the ROI. Similarly, the other 3corner images 574, 576 and 578 each displays the acquired images takenfrom the top right, bottom right and bottom left corners of the ROIrespectively. The central image 580 in window 570 displays the acquiredimage taken from the central part of the ROI.

Window 570 is referred to as a multi-region image, which now allows theoperator to adjust the focus, stigmation, beam shift, and other CPBconditions to obtain the optimal trade-off for best results in all partsof the image. According to an aspect of the present method, the fiveregions scanned have the same area (number of pixels), so each requiresthe same time to scan. In alternate embodiments, more than 5 regions ofthe ROI can be scanned and displayed in window 570. For example, a 3×3matrix showing the center of edges plus the four corners plus the centeris possible. Furthermore, the operator can reposition these regionswithin window 570 as desired, i.e. it is not required that the actualregions scanned have exact positional correspondence to the four cornersand the center, nor that each region scanned have the same area as theother regions.

It is also desirable to apply local image processing to adjust forstigmation or focus issues within different regions of the image oncethe best results are obtained through optimizing CPB conditions. Suchlocal processing can be applied to each of the multiple regionsdescribed above, then the determined values interpolated betweenregions, Determining optimal local settings may be done by a human user,or through image processing techniques.

When two charged particle beams are used for 3D analysis involving bothmaterial removal slice-by-slice and imaging, as shown in FIG. 8, it isadvantageous to be able to track the position or drift of the sample inthree dimensions on a regular basis due to the extended periods of timeinvolved in the process. An approach is described below that allows fora determination of any drift in the “XY” plane as viewed from theperspective of the imaging beam (generated by, for example, an SEM or aGFIS column) as well as determining the rate of advancement (slicingrate or slice by slice thickness in “Z”) of the milling beam (generatedby, for example a LMIS gallium FIB column).

Maintaining knowledge of the position of the cross-section face in threedimensional space is vital to ensuring each slice is at or close to thedesired thickness, and also deriving a knowledge of the actual thicknessof each slice. A 3D positional tracking method is described that enablesthe tracking of the position of the cross-section face during sectioningand imaging in such a way that a direct measurement of the actualposition of each slice can be calculated, allowing for dynamicrepositioning to account for drift in the plane of the sample and alsodynamic adjustment of the forward advancement rate of the FIB to accountfor variations in the sample, microscope, microscope environment, etc.that contributes to drift. An additional result of this approach is theability to dynamically calculate (and potentially report to a dynamicimage processing module) the actual thickness of each acquired slice asit is acquired.

FIG. 17 shows a top-down view of the sample 600 where an initialcross-section 602 has been or is to be fabricated and a first protectivelayer 604 is deposited. A leftmost first notch 606 and a rightmost firstnotch 608 are nanofabricated into or onto (the “notch” may be raised beselectively depositing, rather than removing, material, as discussedbelow) the first protective layer; the leftmost and rightmost firstnotches are together referred to as a first chevron. A leftmost secondnotch 610 and a rightmost second notch 612 form the second chevron. Thisprocess may be repeated multiple times as required, forming the leftmost“nth” notch 614 and the rightmost “nth” notch 616. Or a single notchpair may span the entire first protective layer.

Note that it is also possible to nanofabricate the “notches” directly onthe surface of the sample 600 (in the absence of a first protectivelayer), and it is also possible to fabricate the “notch” as a raisedstructure rather than a groove, i.e, depositing rather than removingmaterial. The term “notch” is understood to refer to a structuredeliberately nanofabricated for purposes of alignment, such as a line orcurve, whose geometry is known. It is often desirable that thisstructure be contiguous, however noncontiguous structures such as adotted line may also be employed, and additional information may begleaned from the “duty cycle” of the “dots”. Multiple dotted lines withdifferent or offset duty cycles may be employed.

In a similar fashion, one or more parallel or nearer parallel “notches”618 may be nanofabricated to serve as known good targets forautofunctions such as auto focus, auto stigmation, auto brightness, autocontrast, etc. as these features can be fabricated to have a known andconstant position on the cross-section face during all or a portion ofthe cross-sectioning process. In the present embodiment shown in FIG.17, three such parallel notches are nano fabricated in the surface ofthe sample 600. These notches 618 extend in a direction orthogonal tothe base of the cross-section face 602. These are all referred to as“autofunction targets” and it is desirable that they have multiple sharpfeatures and interfaces in the cross-sectioned view in order to improvethe execution of autofunctions; such multiple interfaces and featurescan be achieved using one or more notches in dose proximity.

FIG. 18 shows the deposition of a second protective layer over thenotches shown in FIG. 17, which may cover in whole or in part the firstprotective layer. The second protective layer can be a single “blanket”over the areas of interest or it can be selectively nanofabricated tocover just the areas around the notches as shown by the outlines in FIG.18. FIG. 18 illustrates such a selectively nanofabricated protectivelayer composed of multiple regions, including a leftmost firstprotective region 620 and a rightmost first protective region 622 arenanofabricated onto the first chevron and the first protective layer. Aleftmost second protective region 624 and a rightmost second protectiveregion 626 protect the second chevron. This process may be repeatedmultiple times as required, forming the leftmost “nth” protective region628 and the rightmost “nth” protective region 630. Similarly, aprotective layer 632 is deposited over the known good autofunctiontargets.

From a “top down” perspective, a number of notches are nanofabricatedonto the surface of the first protective layer. These notches convergeat a predefined angle and a set of them may have the appearance ofchevrons, although it is not a requirement that the notches meet at apoint nor have any other specific geometry relationship other than thefact they are not parallel and their geometry is known. In FIG. 18, thesample is sectioned in the z-direction, to expose a new x-y surface forimaging.

FIGS. 19A through 19D show multiple cross sectional slices of a samplecontaining a feature of interest. By example, FIGS. 19A through 19D showan x-y plane surface of the sample 600 shown in FIG. 17. It is notedthat the presently shown cross section images do not show the parallelnotches 618. According to the present embodiments, the operator canselect an XROI from these images for the purposes of obtaining a highresolution image of the feature of interest. FIGS. 19A, 19B, 19C and 19Dare images of each cross section of the sample, i.e. the slice of FIG.19D is acquired after (and thus deeper in the z-direction) compared tothe slice of FIG. 19C, which is deeper in than the slice shown in FIG.19B, etc. Progressive changes in size of the feature of interest 664 canbe observed. The cross-section image of FIG. 19A shows a sample 650having a top surface 652, a first protective layer 654, a left chevronnotch 656, a right chevron notch 658, and second protective layers 660and 662 formed over notches 656 and 658 respectively. Shown in thecross-section face is a feature of interest 664. It can be seen fromFIG. 19A to FIG. 19C that the feature of interest 664, shows as acircle, is increasing in diameter as the slices of the sample areremoved. Also, it can be seen that the notches 656 and 658 with theirprotective layers 660 and 662 appear to be moving towards each other.With reference to FIG. 17, notches 656 and 658 correspond to firstnotches 606 and 608. Eventually at FIG. 19D, a second set of notches 666and 668 with their respective protective layers 670 and 672 appear. Itcan be seen that the purpose for having the second set of notches 666and 668 is to continue the depth tracking when notches 656 and 658eventually disappear.

FIGS. 20A through 20D show multiple cross sectional slices of a samplecontaining a feature of interest, similar to those shown in FIGS. 19Athough 19D, except that parallel notches, such as parallel notches 618of FIG. 17 are shown.

The cross-section image of FIG. 20A shows a sample 700 having a topsurface 702, a first protective layer 704, a left chevron notch 706, aright chevron notch 708, and second protective layers 710 and 712 formedover notches 706 and 708 respectively. These features are similar tothose shown in FIG. 19A. FIG. 20A further includes parallel notches 714,of which 3 are shown in the present example. Formed over parallelnotches 714 is a second protective layer 716, composed of the samematerial as second protective layers 710 and 712. Shown in thecross-section face is a feature of interest 718. FIGS. 20A, 20B, 200 and20D are images of each cross section of the sample, i.e. the slice ofFIG. 20D is acquired after (and thus deeper in the z-direction) comparedto the slice of FIG. 20C, which is deeper in than the slice shown inFIG. 20B, etc. The notches 706 and 708 progressively approach each otherin position by FIG. 20C, and at FIG. 20D are shown almost directlyadjacent to parallel notches 714. At FIG. 20D, a second set of notches720 and 722, and their respective second protective layers 724 and 726appear.

FIG. 21 is a perspective view of sample 700 after several sections havebeen taken. The second protective layer over notches 706, 708 andparallel notches 714 is not shown in FIG. 21. In the presently shownexample, the second set of notches 720 and 722 appear in the x-y planeof the cross section face, while the ends of the first set of notches706 and 708 are shown in the same cross section face.

As is readily seen in cross-section, the sample has a surface which hasa certain surface roughness. Depositing a first protective layer overthe surface, could have the additional benefit of providing a degree ofplanarization, smoothing out a portion of the roughness.

In the microscope, it is also more readily observed in cross-sectionthat it is desirable that there be contrast between the first protectivelayer and the second protective layer. One way this can be achieved isif the average atomic number of the material from the first protectivelayer is sufficiently different from the average atomic number of thesecond protective layer. This can be accomplished by depositing one ofthe layers using a heavier (higher average atomic number) materials suchas deposited “platinum” or “tungsten” from a precursor gas such astungsten hexacarbonyl (W(CO)6). Those skilled in the art will realizethat the process of ion (such as Ga+ or He+) or electron beam depositionfrom a precursor gas is well known, and also leads to a “tungsten”deposition that incorporates a mixture of W, C, and the incident beam(Ga, etc.). A lighter (lower average atomic number) material such as“carbon” or “silicon oxide” can be deposited for the other layer. Whenviewed using a detector sensitive to the average atomic number (i.e. onesuch as the Carl Zeiss Energy Selective Backscatter Detector, EsB),regions of higher average atomic number have higher signal (brighter)and regions of lower average atomic number have lower signal (darker).Note also that the EsB allows imaging of SEM generated electrons of acertain energy and filters out the FIB generated electrons duringsimultaneous milling and imaging.

It is also possible to achieve the desired contrast between the firstand second layers using a single gas precursor, and depositing one layerusing a first beam (say an ion beam such as Ga+, He+, Ne+ or Ar+ byexample) and a second layer using a different beam (an ion beam of adifferent species or an electron beam). In the case of using a Ga beamfor one layer and an electron beam for another, the average atomicnumber of the two layers would be different due to factors such as theincorporation of the Ga into one layer (whereas the deposition by anelectron beam would not leave an elemental species incorporated in thelayer), differences in density of the layer due to different chemicalprocesses arising from the deposition method, etc.

It is also possible that the one or both of the protective layers isomitted, and the contrast arises between the features nanofabricatedinto or onto the sample and the sample itself (and any protective layersthus employed).

In the presently disclosed embodiments, the notches are used asalignment marks, ie. patterns in the sample that are such that whenimaging the cross-section face, the distance between the marks allowunique identification of the position of the cross-section plane alongthe Z axis. These alignment marks can be repeating structures that onlyallow unique identification of their position when a coarse position ofthe cross-section is also known, and can be patterned directly into thesample surface using the patterning beam. In one example, the notchesare patterned such as to produce a suitable contrast when imaging thecross-section. For example, the alignment mark generated by firstdepositing a Pt or W layer on the sample surface, milling the marks andthe depositing a C layer on top. This arrangement results in a highcontrast image with most imaging beams and detectors. The secondprotective layer can be a material with high contrast relative to thematerial of the notches, to further enhance notch patter recognition bythe CPB workstation 100, since the combination of the contrasting layerswould be unique in the image, and thus easily detectable by the systemfor auto depth calculations. This feature is now described in moredetail.

Referring to FIG. 22, mathematically, a pair of notches are idealized assegments in the XZ plane with all points in the segments parametrizedas:

$\begin{matrix}{\overset{\rightarrow}{r_{L}} = {{\overset{\rightarrow}{n_{L}}\mspace{11mu} s} + \overset{\rightarrow}{r_{OL}}}} & \; \\{And} & \; \\{\overset{\rightarrow}{r_{R}} = {{\overset{\rightarrow}{n_{R}}\mspace{11mu} s} + \overset{\rightarrow}{r_{OR}}}} & \; \\{With} & \; \\{{\overset{\rightarrow}{n_{\frac{L}{R}}} = \begin{pmatrix}\frac{n_{xL}}{R} \\0 \\\frac{n_{zL}}{R}\end{pmatrix}},{\overset{\rightarrow}{r_{\frac{OL}{R}}} = {{\begin{pmatrix}\frac{x_{OL}}{R} \\\frac{y_{OL}}{R} \\\frac{z_{OL}}{R}\end{pmatrix}\mspace{14mu} {and}\mspace{14mu} s} \in {\left\lbrack {0,1} \right\rbrack.}}}} & \;\end{matrix}$

Where these notches intersect the XY plane at z=z_(CS), the distancebetween them is

$\Delta_{RL} = {{x_{R} - x_{L}} = {{{\frac{n_{xR}}{n_{zR}}\left( {z_{CS} - z_{OR}} \right)} - {\frac{n_{xL}}{n_{zL}}\left( {z_{CS} - z_{OL}} \right)} + \left( {x_{OR} - x_{OL}} \right)} = {{\left( {\frac{n_{xR}}{n_{zR}} - \frac{n_{xL}}{n_{zL}}} \right)z_{CS}} + {\Delta_{ORL}.}}}}$

If this distance is measured at some time taken as the origin and a timet later, then the precise position of the cross-section time can bedetermined from the change in distance between the notches:

${z_{CS}(t)} = {\frac{{\Delta_{RL}(t)} - {\Delta_{RL}(0)}}{\frac{n_{xR}}{n_{zR}} - \frac{n_{xL}}{n_{zL}}} + {z_{CS}(0)}}$

If the notches are patterned at ±45°, then

$\frac{n_{x}}{n_{z}} = {\pm 1}$

and this simplifies to:

z _(CS)(t)=Δ_(RL)(t)−Δ_(RL)(0)+z _(CS)(0)

The change in distance between the notches is twice the change in z ofthe cross-section position. This means that under these conditions, witha precision of 2 nm when measuring the distance between the notches, theposition of the cross-section can be determined to within 1 nm.According to the present embodiments, the method of FIG. 7 can includethis calculation as frequently as is determined to be necessary by thecalculated drift model, or as desired by the user, this calculationbeing performed based on high resolution XROI imaging of the expectedlocation of the notches (iteratively if necessary, with expansion and/orrepositioning of the notch imaging XROI if the first XROI image passdoes not find the notch where it was expected to be). From these notchXROI images, the notches can be automatically identified by the systemand a determination is made from the previous notch position theapproximate depth the current cross-section image relative to theprevious low resolution cross section image (key frame). From thiscalculation, if the current cross-section depth is thicker than desired,then the milling rate is decreased. Otherwise, if the currentcross-section depth is thinner than desired, then the milling rate isincreased. This rate of increase or decrease of the milling rate of theFIB can be calculated by the CPB workstation 100, which in turn controlsthe FIB accordingly. FIG. 23 is a flow chart illustrating a sub-processfor controlling the FIB sectioning rate, which can be used incombination with the method of FIG. 7.

In FIG. 23, it is assumed that a key frame image, or even one or morehigh resolution images (which may be XROIs), of a region of interest ofa sample having the aforementioned notches formed therein has beenacquired. At 800, the faces of a pair of notches are identified from theimage data. This can be done manually by having the operator input thenotch positions, or automatically via pattern recognition routinesexecuted by CPB workstation 100. Then the x axis positions of thenotches are compared with the x-axis positions of the notches from aprevious image at 802. The CPB workstation can then calculate theapproximate real thickness of the section, being the material removedsince the last image that resulted in the current image. A comparison ismade between the real calculated thickness to the desired thicknessinput by the operator at 804. Based on this difference, a new FIB milladvancement rate is applied to the CPB system 10 which would result inthe next section having a real thickness that matches the desiredthickness. If there is no difference, within a predetermined margin oferror, then no change to the FIB milling rate is made. In an optionalstep 808, at least one additional compensation process can be executedusing the notches. These additional compensation processes are nowdescribed.

According to the present embodiments, the notches are imaged in thecross section view and can be used by the CPB workstation 100 to executeother different operations. The CPB workstation 100 can calculate theposition of the mill based on the alignment marks, compared to where itis expected to be based on the intended position of the milling beam.Therefore, it is possible to determine the amount of drift of the samplerelative to the milling beam. The source of this drift, be it stage,sample, beam electronics, etc. is unimportant, as it simply relates tothe actual versus estimated position of the milling beam relative to thesample. A suitable model can be used to project future drift based onobserved values and preemptively adjust the milling beam position tomatch a target milling rate.

In other application of the notches, the position of the sample surfacein the X and Y plane (cross-section imaging position) can be determined.By calculating shifts in the image based on the notches, it is possibleto determine the amount of drift of the sample relative to the imagingbeam. The source of this drift is unimportant, it simply relates to theactual versus estimated position of the milling beam relative to thesample. A suitable model can be used to project future drift based onobserved values and preemptively moderate the imaging beam position tominimize any drift that may occur during the image acquisition.

In yet another application, the CPB workstation 100 can normalize theintensity of the image based on the light and dark portions of theprotective layers above and below the notches. When the notches arecreated as a stack of two materials of different CPB imaging contrast,the histogram of an image of the bilayer above and below the notcheswill generally be bimodal. The average and spread of the two modes ofthe histogram can be used to evaluate or compensate for brightness andcontrast changes of the detector or the beam current, etc. of the CPBbeam itself.

Additional applications for the notches include:

1) Automatic sample realignment. Using multiple fiducials would allowfull and precise repositioning of the sample in the event the stage wasmoved or the sample unloaded.

2) Automated aperture alignment, in the event multiple milling aperturesneed to be used.

3) Automatic realignment in the event of a glitch (or power off/poweron) of the milling beam.

4) Autofocus and autostigmation of the milling beam.

FIG. 24 shows data from the execution of the 3D tracking methodaccording to the previously described embodiment, operating on a samplewhose desired slice thickness is 5 nm. The FIB Positional Error, whichis effectively the measured error in the slice thickness, is plottedversus the time since the data acquisition run commenced. As shown inFIG. 24, during the first hour or thereabout after the sample has beenloaded in the microscope and the run has begun, the drift rate istypically very high. In the absence of this algorithm this leads toslices that would be 25 nm or more in thickness (or 0 nm if the sampledrifts away from the patterning beam). As can be seen in FIG. 24, thepreviously discussed tracking method rapidly reduces the error in theslice thickness to a nanometer or two. Note that approximately ten ofthe many hundred measurements are shown as open diamond symbols, ratherthan filled diamonds. These measurements are determined to likely be dueto imaging error, as determined by algorithms that may compare thequality of the image of the notch, the likelihood, based on thepredictive drift model, environmental feedback, etc., that the positionof the notches as determined by the imaging of the notches is correct,etc. In the event this error checking algorithm determines there maypotentially be a problem with the latest images of the notches, thisalgorithm can be set to trigger one or more iterative re-imaging stepsto reduce or eliminate this error.

It is advantageous to correct for any drift in a predictive manner,allowing for a smooth, adaptable adjustment of both the milling andimaging beam position (or adjustment of just one beam) to predictivelycorrect for any error measured from the fiducial marks discussed above,commonly referred to as “drift”. It is often optimal that this smoothcorrection for drift be performed as a series of small correctionsseparated in time, rather than larger corrections that are morediscontinuous in nature.

A slice of the sample is milled away by rastering the milling beamaccording to a pattern that is predominantly perpendicular to thethickness of the slice to be removed. In its simplest implementation,the milling beam is rastered in a single line, perpendicular to theslice thickness. As the beam is rastered, it is continuously ordiscretely shifted along the direction of the slice thickness with annominal average linear progression rate in the direction of the slicethickness of ν_(n). After time Δt, a slice of nominal thicknessΔi_(n)=μ_(n)Δt will have been removed. By imaging the fiducial notchesat times t₀ and t₀+Δt, it is possible to determine the actual thicknessof the slice Δl or equivalently the actual average progression rate ofthe beam

${v = \frac{\Delta \; l}{\Delta \; t}},$

and therefore infer the amount of drift of the entire system in thedirection of the slice thickness, This drift is primarily comprised ofdrift of the milling beam due to electronic stability, physical sampledrift as well as beam displacement induced by the interaction of themilling and/or imaging beams with the sample.

At certain intervals t_(i), which might be for every slice of for anumber of slices, the effective drift rate of the system ν_(d)(t_(i))can thus be estimated. Given this estimate of the drift rate, it ispossible to preemptively and continuously adjust the milling progressionrate ν_(m) to include the drift rate and therefore produce slices thathave thicknesses closer to the target nominal thickness. In the presentembodiments, the milling beam progression rate ν_(m) is dynamicallyadjusted at each time t_(i) base of the new measurement of the realprogression as well as on past measurements [ν(t_(i))] in an effort togenerate the nominal milling rate ν_(n). A reader skilled in the artwill recognize that this system is one where current and pastmeasurements are used as feedback to predictively compensate for systemerrors and instabilities to recover a nominal target.

Such a system can be solved by implementing a control-loop feedbackmechanism such as one implemented in a proportional-integral-derivative(PID) controller. A block diagram of a known controller is shown in FIG.25. In such a controller, where the error e(t_(i))=ν_(n)−ν(t_(i)) is thedifference between the nominal milling rate and the measured millingrate, the correction to the milling drift rate is calculated based onthe current error (proportional component), the integral of all past andpresent errors (integral component) and derivative of the current error(derivative component).

The determination of the optimal parameters (K_(P), K_(l), K_(D)) foraccurately and reliably predicting the proper milling progression rateis beyond the scope of this document. In a simple implementation, theproportional, integral and derivative coefficients of the controller canbe fixed by design, and the controller is simply used to calculate themilling rate applied to the beam based on all measurements of slicethicknesses. If the control mechanism is stable, this will result inslice thicknesses that are closer to the nominal target slicethicknesses. This is particularly relevant at very small nominalprogression rates when the drift rate is comparatively large. Withoutcompensation, the slice thicknesses could potentially be much too largeresulting in loss of sample information.

The position of the fiducial notches can also be used to compensate forsystem drift as observed by the imaging beam. Prior to acquiring animage, the relative position of the notches is used to determine the zposition of the cross-section face, and their absolute position can beused to re-centre the fiducials in the x-y imaging plane. Note thatalthough only a single notch is necessary, using multiple notches allowsmore flexibility and more robustness in the calculation of the drift. Inparticular, it can be used to virtually eliminate surface topographyeffects by choosing which notches to use. It may be beneficial toperform this step during the acquisition process and not simply duringpost analysis as it allows the target volume of interest to be properlytracked and imaged efficiently. By example, steps 800 and 802 can beexecuted in combination with step 300 of FIG. 7, just prior toacquisition of the image to compensate for any drift.

Without correction, it may be necessary to image a larger volume thannecessary to ensure that the target volume is acquired. Given thepotentially very long acquisition times (possibly several days), thesystem stage may drift several microns under normal operatingconditions, so it is advantageous to frequently adjust for this drift toobtain consistent and reliable results.

In addition to statically adjusting the imaging beam shift to re-centrethe features prior to acquiring the image, it is also possible tocorrect for drift during the image based on the current, past and futuremeasurements of the fiducial positions. As a first implementationperformed during post-processing, given a measurement before and afteran image, a calculation can be performed determine the average driftthat occurred during the image and to skew the image to compensate forthis drift: if a drift {right arrow over (d)}=(d_(x),d_(y)) is measuredbetween times t and t+Δt, then an image scanned left to right and top tobottom can be skewed or otherwise adjusted during the scan (or lessoptimally during post-processing) according to FIGS. 26B, 26C and 26D torecover a more realistic representation of the sample.

FIG. 26B to 26D illustrates the track of a beam on the sample beingdistorted from a diamond XROI as shown at FIG. 26B, to the distortedshapes shown in images of FIG. 26C and FIG. 26D. A reference fiducial1400 is shown in FIG. 26B. The original image 1402 drifts from slice toslice to distorted images 1406 as shown by cumulative drift 1408 and1412 in FIGS. 26C and 26D, respectively. This distortion is evident evenin the presence of classic drift correction that is applied “all atonce” at the beginning of each image pass.

In an example embodiment, a predictive dynamic drift correction is usedto “de-skew” this distortion during scanning. At the end of the scan thepredicted drift and the actual drift may be compared, and if necessary,a correction skew may be applied in post processing the image.

The predictive dynamic drift correction uses a Predictive Model of Driftthat allows continuous sub-pixel compensation. In implementation, one ormore fiducial(s) 1400 are monitored for the time taken to acquire theimage 1402. A drift factor is calculated for example, based on a driftof the fiducial(s) 1410) and the drift correction factor is applied onthe subsequent frames, for example, to FIG. 26C and FIG. 26D based on“picometer” beam correction—by applying infinitesimally small correctionto each pixel. The correction is typically well below DAC granularity,so that the correction results in a “smooth” correction throughoutimage.

The dynamic drift correction results in the corrected image scanning theexact desired region, similar if not identical to FIG. 26B and not asshown in FIG. 26C and FIG. 26D obtained due to continuous drift duringimaging, which outline classic drift correction which takes placeperiodically (often at the end of an image) as a single motion. Bystoring applied drift vector for every image (which may or may not beapplied linearly), the drift correction can also be “removed” from theimage if it proves to be a poor estimate, or re-corrected, which resultsin a somewhat skewed imaged area, but most likely not as badly skewed aswith “Classic” drift correction.

In other example embodiments, the dynamic drift correction may beapplied based on environmental monitoring. For example, drift correctionmay be applied as a function of spatial and environmental measurements,such as temperature, pressure, sound etc. The impact of theseenvironmental factors on the drift may be modeled as a function ofchange in the environmental factor (for example, how a 1 degree changein temperature affects the drift) and the drift correction may beadapted to incorporate these measurements and modeled impact in realtime. This enhances the dynamic drift correction capabilities andimproves the quality of imaging when an image may take several minutesto acquire.

In a further example embodiment, the dynamic drift correction may alsobe used to interrupt an image periodically to drift correct. However,the interruption may result in “jumps” in the image and hence it may bepreferable to correct the drift as a smooth function based on thepredictive model, and perhaps update the predictive model periodically(e.g. multiple times within a scan) instead.

According to a present embodiment, a drift correction method is executedduring live imaging as in the case of the preemptive milling correction.Given past drift measurements, a preemptive beam shift is applied duringthe imaging to compensate for the expected drift during the image,negating the need for post-processing. Again, as in the case of themilling correction, a PID controller can be used to estimate the amountof drift necessary to compensate for the system drift and eliminate anyactual shift in the image. For very long image acquisitions, the amountof drift at various times during the image can be re-evaluated (e.g.pause at the end of a line, image the notches, then resume the imaging)in case the required drift correction changed during the acquisitiontime. Note that the drift correction to be applied can be calculated toa level that is well below the least significant bit on the DAC. Forexample, the total drift correction calculated to be applied during animaging sequence can be divided by the toal number of dwell periodsduring said imaging sequence, and this differential drift correction perpixel can be added to the engine at each and every pixel dwell, and whenthe cumulative correction becomes large enough to exceed one DAC LSB,the correction effectively shifts the beam by one LSB, and theaccumulation of correction continues.

In the examples discussed thus far, the “slice” of material removed byan ion beam is generally removed in a geometry such that the ion beam isat a glancing angle to the surface being sliced. It is also possible toremove material using an ion beam whose angle of incidence is muchcloser to the normal to the surface being sliced. In general, it is wellknown in the art (especially from the field of SIMS) that this approachwill develop topography, especially if the sample is not homogenous. Gasbased chemistries can be used during milling (also known asNanoPatterning or patterning) to enhance the removal rate of a material,sometimes selectively compared to the removal rate of another material,but when the patterning beam deviates from near glancing incidencetopographic artifacts as well as artifacts due to uneven gas flow tendto disrupt the uniform removal of material and disrupt the desired “flatbottom” structure necessary for such processes as circuit editing or 3Dreconstruction.

The following probabilistic patterning embodiment is used formaintaining a “flat bottomed” structure desired for circuit editing or3D reconstruction. Most patterning involves repeating the same simplepattern over and over for a given amount of time. In the case of regularshapes such as rectangles, this results in a shape that has been exposedto a uniform dose, and will consequently have eroded or deposited auniform amount of material, subject to milling or deposition effects(particularly on the periphery of the NanoPatterned areas) that are wellknown in the art to occur.

To expose a shape to a non-uniform dose, commonly known as “bitmap” or“greyscale” milling, several implementations exist. The most commonapproach is to vary the dwell time at each pixel of the shape based onthe grey level of its corresponding pixel in the shape mask. Thisresults in a dose distribution that is a copy of the dwell timedistribution. Another approach is to pattern the shape as a sequence ofslices corresponding to the various grey levels in the mask bitmap. Thisalso results in a final dose distribution that matches the grey-leveldistribution in the mask image. It should be noted that as early as1995, Micrion Corporation of Peabody, Mass. incorporated a means topattern using this second approach, with the “bitmap” generated atpredetermined intervals from the signals (image) generated by the ionbeam.

The advantage of the first method is that each pass delivers a properdistributed dose and that all the pixels are visited for each pass, soif the pattern is stopped at any point in time, the actual dosedistribution is proportional to the target distribution. However, duringeach pass, the local dose delivered per pixel is not constant: pixelswith longer dwell times receive more dose that those with shorter dwelltimes. In the case of gas assisted etching or deposition, where the doseper pixel must be closely regulated to avoid gas depletion and optimizemilling efficiency, this results in inefficient and sometimes impropermilling conditions. The second method does not suffer from this problemsince each slice is patterned with the proper dwell and pixel spacings,thereby ensuring gas chemistry to be optimal. However, since thepatterning of each slice is occurring as subsequent steps, the properdose distribution is only obtained after all the slices are processed.

To resolve issues with the existing approaches, the presently describedprobabilistic patterning method is employed, an embodiment of which isshown in FIG. 26A. The scan pattern for a region of interest input bythe operator is configured at 850 by the CPB workstation 100. The scanof the sample is executed at 852 and the beam is iteratively positionedat each dwell point within the pattern. At 854, the scan generatoralgorithm will choose whether it will dwell on a dwell point or skip itwith a probability supplied as the mask bitmap. If the dwell point isskipped, then the beam is not activated, and the beam is positioned forthe next dwell point at 860. Otherwise, the beam is activated at 858 andthe beam is positioned for the next dwell point at 860. The method loopsback to 854 for a determination if the current dwell point is to beskipped or not.

The result of this approach is that for each pass, the proper dwell timeis applied to all of the dwell points, resulting in proper gas assistedprocesses, and at any time the average local dose will be correct. Basedon the central limit theorem of probability theory, the patterned dosedistribution at each dwell point will converge to the targetdistribution given a large enough number of passes. In the presentembodiment, the operator has the option to control the number of passesof the pattern by the beam. The greater the number of passes, the closerthe actual dose distribution will reflect the target dose distribution.

Although this approach is probabilistic and may be implemented using anumber of well known methods of generating random numbers,implementation is accomplished by using a known random number sequenceto accept or reject dwell points at each pass, which results in a verydeterministic stream of dwell points that can readily be deconvolutedduring imaging to determine where the beam dwelled and where it did not,thus allowing the resultant signals from the target to be imaged,thereby allowing the patterning to be constantly monitored in real-time,allowing proper visual endpointing. Indeed, it is possible to apply theapproach of Micrion Corporation to determine, via this signal collectedat known increments, how to evolve the milled (or otherwise patternedarea) over time, based on the image, reconstructed periodically, fromthe signals and deterministic stream of dwell points.

In addition to resolving both issues of instantaneous dose distributionproportional to target dose distribution, and proper pixel dose perpass, this technique also resolves other issues present when using gasassisted etching. One common example occurs when using XeF2 to assistthe etching of silicon, once exposed to the beam, the silicon continuesto be etched spontaneously for a short period of time. This spontaneousetching typically is not uniform and results in pitting of the surfacewhich may get accentuated by further milling and will result in anon-flat mill. When using a variable dwell method, the total frame timemay be quite long because of the dwell multiplier (a 1000×1000 pixelpattern with a base dwell of 100 ns and an average multiplier of 128will have a frame time of more than 12 s). When using the slice basedapproach, in portions of the pattern that do not get many passes, thedelay between the slices where the beam is unblanked may be too long,thereby resulting in pitting in these areas.

When using the probabilistic patterning embodiments, even if an area hasa low overall probability, the probability that at least one pixel inthe vicinity will be patterned at each frame can be quite high, So eventhough for a given pixel, the time between visits may be large enough tocause problem with self etching if its milling probability is low, thetime between visits in its vicinity will be such that self etching canbe inhibited. For example, if an area of the pattern has a probabilityof 20% of being patterned (⅕ of the total dose is desired), then for asquare dwell point arrangement where each pixel has 8 neighbours, thenthe probability of at least one of the neighbours of being visited ateach pass is almost 90% (P=1−(1-20/100)9), which direct experimentationhas proven to be sufficient to reduce the spontaneous etch issues usingXeF2.

A benefit of the probabilistic patterning embodiment is the reduction ofthe current density of the incident beam on the sample, which can reducecharging artifacts during imaging.

Probabilistic milling can also be used for top-down or nearer normalincidence removal of material for 3D activities, rather than nearglancing angle. Scanning the beam in a probabilistic pattern accordingto the previously described embodiment, rather than in the conventionalsequential method, reduces differential milling artifacts that arisefrom sample features, leading to a more planar removal rate. Note thatthe sample can be monitored using either the signal generated by thepatterning beam during probabilistic patterning, or by another imagingbeam such as an electron beam directed at the area of interest. Byhaving the imaging beam off-axis from the patterning beam, athree-dimensional view can be reconstructed whose information can beused to alter the probabilistic patterning probability distribution tosmooth out (or enhance if desired) variations arising from sampleinhomogeneity.

As previously mentioned, the 2D and 3D imaging embodiments can benefitfrom additional improvements over control of the CPB system 10, whichcan be provided by CPB workstation 100. These are referred to asmulti-pass rastering, spatial super-sampling and temporal sub-sampling,which can be optionally enabled during the imaging phase in order toimprove data quality or optimize a particular component of the signalthat is used to generate the image.

During any beam raster operation executed by CPB system 10, whichincludes imaging, milling, gas assisted etching or deposition, the FIBbeam deflection software and hardware deflects or positions the beam ina preset pattern across the surface, generally referred to as rastering.At each preset location, the beam is left to dwell for a given period oftime before moving to the next point in the raster. At its simplest, araster pass consists of positioning the beam at fixed increments alongone axis from a start point to an end point, dwelling for a fixed dwelltime at each point. At the end of a line, the beam waits a fixed retracetime before moving an increment in a second axis. The beam may return tothe start point in the first axis and begin again, or may begin“counting down” the first axis from the point it had just reached(depending on whether the raster type is raster (the former) orserpentine (the latter). This process continues until all increments inboth axes have occurred, and the beam has dwelled at all points in thescan. The typical spacing between each point along a raster isdetermined based on the scan size and the digital scan generator. Thesefactors affect the resolution of the scans as discussed below.

Many CPB systems use 12 bit high speed deflection on a 12 bit scangenerator. Dwell time per point is typically less than 1 μs. One exampleis the Vectra FIB system from FEI Company of Hillsboro Oreg., which iscapable of achieving a focused spot with significant beam current thatis less than 20 nm in diameter. When operating with a 20 nm spot at a320 μm FOV at the maximum limit of the 12 bits of the scan generator,the spacing between scan points, Δx_(Scan) and Δy_(Scan) will be aboutfour times the spot size (320 μm/4096≈80 nm). This results in asituation such as that shown diagrammatically in FIG. 27, where a singleraster pass “A” generates dwell points that are on a grid with spacingsΔx_(Scan) and Δy_(Scan), each Δx_(Scan) and Δy_(Scan) equalling about 80nm.

As can be seen from FIG. 27, under these beam and scan conditions only1/16^(th) of the total area nominally being scanned is actually havingthe beam incident upon its surface because of the limitations of the 12bit scan generator. Although under these beam conditions the CPB iscapable of a 20 nm spatial resolution, it cannot address points 20 nmapart under these scan and FOV conditions, hence its resolutioneffectively becomes limited by the 12 bit scan generator. Assuming norandom drift occurs, the next raster pass “B” of the scan will place thebeam at exactly the same locations, with exactly the same problems, asshown in FIG. 28.

The raster scanning method according to a present embodiment improvesthe spatial resolution of the scans by controlling the beam along araster. The method advantageously uses the finer placement controls forthe beam available on CPB systems. For example, the Vectra can positionthe beam with much finer placement through a control known as the beamPan (analogous to a beam Shift or other offset voltage as applied inother CPB systems). On the Vectra and similar systems, the magnitude ofthe Pan deflection is independent of the field of view, and the minimumPan increment is on the order of the smallest spot size achievable,although the speed with which the Pan can be varied is typically muchslower than the deflection. Thus, even at the 320 μm FOV considered inthe earlier example, it is possible to deflect the beam, using the Pan,by an increment that is much smaller than the scan increments Δx_(Scan)and Δy_(Scan), which are both −80 nm at a 320 μm FOV.

Generally, the method may be implemented in an example embodiment asillustrated in the flowchart of FIGS. 29A and 29B, where FIG. 29B is acontinuation of FIG. 29A. When a scan is started 1002, the CPB system isset up for raster pass 1004 to collect data. The beam is positioned at astart dwell point in the raster 1006 and allowed to dwell at the startdwell point for a selected period of time 1008. The beam is thenrepositioned the beam at a subsequent dwell point along the raster 1110and allowed to dwell at the subsequent dwell point for the selectedperiod of time 1012. The repositioning and dwelling is iterativelyperformed at subsequent dwell points until the end dwell point in theraster is reached, i.e., the end of raster pass is reached 1014. Eachsubsequent dwell point is defined by a fixed spacing from its previousdwell point. As described earlier, the fixed spacing or increment is afunction of a scan size and a resolution of the digital scan generator.

For improving the spatial resolution, one or more offset raster passesare performed 1016. In an offset raster pass, the beam is repositionedthe beam at a position offset from the start dwell point in the raster1016, once the end of the raster is reached. The offset is less than thefixed spacing, and may be determined, for example, as function of thebeam size. The beam is then allowed to dwell at the position offset fromthe start dwell point for the selected period of time 1018. The beam isthen iteratively repositioned at subsequent offset dwell points alongthe raster 1020 and allowed to dwell at each subsequent offset dwellpoint for the selected period of time 1024 until the end dwell point inthe raster is reached 1026. Each subsequent offset dwell point isdefined by the fixed spacing from its previous offset dwell point.

At the end of an offset raster pass, if additional offset raster passesare desired 1028, further offset raster passes are performed 1030 andadditional offset is applied to the beam 1032 for setting up the nextoffset raster pass. Upon completion of the multiple-raster passes and nofurther offsets remain in the current raster, the raster is advanced tothe next raster in the scan 1036. Upon completion of multiple-rasterpasses for each raster in the scan, i.e., when the end of scan isreached 1034, the process is stopped 1040.

With reference to FIG. 30, consider a case where the Pan incrementsΔx_(Pan) and Δy_(Pan) could be set to 20 nm, namely one quarter of thescan increments (Δx_(Pan)=¼Δx_(Scan)). By performing a first raster pass“A” with Δx_(Pan)=0 nm, then setting the Δx_(Pan)=20 nm, and performinga second raster pass “B”, the sample would have been exposed to the beamin the manner as shown in FIG. 30.

According to an embodiment of the present invention, this generalapproach can be repeated a total of 16 times, over raster passes “A”through “P”, sequentially changing Δx_(Pan) and Δy_(Pan) at thecompletion of each raster. Within each Δx_(Scan) and Δy_(Scan), sixteendifferent Δx_(Pan) and Δy_(Pan) could be set, according to Table 1below:

TABLE 1 A B C D X_(Pan) = 0 nm X_(Pan) = 20 nm X_(Pan) = 40 nm X_(Pan) =60 nm Y_(Pan) = 0 nm Y_(Pan) = 0 nm Y_(Pan) = 0 nm Y_(Pan) = 0 nm E F GH X_(Pan) = 0 nm X_(Pan) = 20 nm X_(Pan) = 40 nm X_(Pan) = 60 nm Y_(Pan)= 20 nm Y_(Pan) = 20 nm Y_(Pan) = 20 nm Y_(Pan) = 20 nm I J K L X_(Pan)= 0 nm X_(Pan) = 20 nm X_(Pan) = 40 nm X_(Pan) = 60 nm Y_(Pan) = 40 nmY_(Pan) = 40 nm Y_(Pan) = 40 nm Y_(Pan) = 40 nm M N O P X_(Pan) = 0 nmX_(Pan) = 20 nm X_(Pan) = 40 nm X_(Pan) = 60 nm Y_(Pan) = 60 nm Y_(Pan)= 60 nm Y_(Pan) = 60 nm Y_(Pan) = 60 nm

This would result in a more optimal mapping of the field of view, whereafter this 16 raster pass operation was complete, the dwell points datacould be reconstructed to produce an image where all dwell points werecontiguous. FIG. 31 illustrates the resulting mapping after implementingan embodiment of the present invention.

According to another embodiment of the present invention, the previouslydescribed method could be further refined by modifying the Pan variationalgorithm to operate in a “serpentine” manner, where between eachsequential raster pass “A” through “P” only a very small change in thePan settings would be required. Such small changes could likely beaccommodated in a more stable manner by the slower speed Panelectronics. Table 2 illustrates a raster pass mapping according thepresently described embodiment of the invention.

TABLE 2 A B C D X_(Pan) = 0 nm X_(Pan) = 20 nm X_(Pan) = 40 nm X_(Pan) =60 nm Y_(Pan) = 0 nm Y_(Pan) = 0 nm Y_(Pan) = 0 nm Y_(Pan) = 0 nm H G FE X_(Pan) = 0 nm X_(Pan) = 20 nm X_(Pan) = 40 nm X_(Pan) = 60 nm Y_(Pan)= 20 nm Y_(Pan) = 20 nm Y_(Pan) = 20 nm Y_(Pan) = 20 nm I J K L X_(Pan)= 0 nm X_(Pan) = 20 nm X_(Pan) = 40 nm X_(Pan) = 60 nm Y_(Pan) = 40 nmY_(Pan) = 40 nm Y_(Pan) = 40 nm Y_(Pan) = 40 nm P O N M X_(Pan) = 0 nmX_(Pan) = 20 nm X_(Pan) = 40 nm X_(Pan) = 60 nm Y_(Pan) = 60 nm Y_(Pan)= 60 nm Y_(Pan) = 60 nm Y_(Pan) = 60 nm

This “serpentine” mapping technique preferably uses software configuredto activate the necessary Δx_(Pan) and Δy_(Pan) settings between rasterpasses, as well as to reconstruct the resultant new image at highresolution as a mosaic of 16 separate raster passes. Such software orfirmware can be written, or existing control software can be modified.

It is noted that in the above example embodiment, upon completion of thefirst raster pass (with no offset), the beam is repositioned at thestart dwell point with the offset applied. In other example embodiments,the beam need not be repositioned at the start dwell point (with theoffset) for the subsequent raster, but the subsequent raster maycommence at the end dwell point, by positioning the beam at a positionoffset from the end dwell point and rastering in a direction opposite tothe previous raster. This process is referred to as “doubleserpentining.”

An advantage of this technique is the virtue of requiring no majorchanges to the deflection electronics. It is noted that if thedeflection power supplies prove to be insufficiently stable, they can bereplaced with more stable units without effecting the other componentsor control logic. It is further noted that the presently describedembodiments are not limited to 16 raster passes.

Another advantage of this embodiment is the fact that, as the CPB systemis designed to accept Pan changes during rastering, this method could beimplemented without requiring a change to the raster control softwaremerely by setting up a system to set the necessary Δx_(Pan) and Δy_(Pan)settings at the appropriate points in time. Otherwise, the rastercontrol software would operate normally and yield the correct values fordose per unit area, etc. As CPB systems such as the Vectra are designedwith a “refresh” interval, whereby the beam pauses for a definableamount of time at the end of each raster pass, setting the appropriateΔx_(Pan) and Δy_(Pan) settings could be accomplished in software duringthe refresh time at the end of a raster. Another approach would be toexamine the vertical retrace signal generated in hardware by the rastergenerator and make appropriate modifications to the Pan values when avertical retrace signal was detected.

Dwell point analysis software such as FIB Assist from FibicsIncorporated of Ottawa, Canada could be configured to assemble theappropriate “human-readable” high resolution images from such anapproach as well as set the necessary Δx_(Pan) and Δy_(Pan) settings atthe appropriate points in time. By providing an appropriate userinterface, such a system could theoretically achieve 20 nm spatial andplacement resolution anywhere within a 320 μm (+/−160 μm) field of viewwithout resorting to stage motion, on the existing Vectra systemelectronics.

An example implementation of he aforementioned embodiments of thepresent invention is now be described.

In this method, all image and mill commands generated by the user are“filtered” by control software, which enable the user to position thestage at a fixed point and operate within a 320 μm FOV, moving as ifthey were moving the stage, but without stage motion. A typicalimplementation entails the user performing imaging and enabling millsanywhere within that 320 μm FOV, at “Effective” Fields of View (EFOV)from less than 1 μm up to 320 μm in this mode.

A user request for a standard image pass for a 1,024×1,024 image isintercepted by the CPB workstation 100 and turned into a request for 16rasters of a mill with 256×256 dwell points, plus implementation of thenecessary Pan adjustments (4 adjustments to the Δx_(Pan) and 4adjustments to the Δy_(Pan)) in an automated fashion. Note that thenumber of Pan adjustments required for these algorithms are smallcompared to the full Pan range, so the user would not see a significantreduction in the Pan range available to them through implementation ofthis technique. The process of defining and building the image ishandled in software and is virtually transparent to the user.

To simplify visualization of such an approach, consider an EFOV of 10 μmthat can be “scrolled” to anywhere within +/−160 μm of the stage centerin High Resolution Navigation (HRN) mode. Using appropriate controls,the user enters this HRN mode, and stage readback, Knights navigation,EFOV, etc. would function as if the user was moving the stage.

The system can optionally apply the Pan corrections on a line by linebasis during imaging, in order to generate full resolution lines one ata time, rather than, as in the scheme, above full resolution frames oneat a time. In other words, it may be preferable to perform multiplerepeats of the same line with Δx_(Pan) corrections applied during thehorizontal retrace until the full resolution line is composed anddisplayed to the user, before proceeding to the next line,

The Vectra possesses the necessary “Line Scan” algorithm to raster amill while delivering the full dose to each line before proceeding tothe next line. In this case the user sees a high resolution image builtup on a line by line basis, that appears identical to the image that isformed during a conventional image pass. This image is responsive tofocus and stigmation in the same manner as a conventional image. Such aline by line process can be applied to milling as well as imaging,however the scheme outlined above may be more suitable to the very shortdwell times required for gas assisted milling operations, whereas thelonger dwell times typical of imaging operations could more easilysupport the formation of the high resolution image on a line by linebasis.

By appropriately intercepting all calls for imaging and millingoperations at a given EFOV, and recasting them to incorporate the Panadjustment scheme and data processing at the real FOV, the user will notbe aware of the “machinations” occurring in the background, but wouldinstead appear to have gained a 16 bit (or higher) deflection systemwhere they previously had only 12 bits. On systems with a higher nativeDAC resolution, this approach can yield similar improvements.

To simplify the process and avoid errors arising from the granularity ofthe Pan deflections themselves, the number of EFOVs available in HRNmode can be fixed to a few EFOVs at optimal values ranging from 0.25 μm,to 320 μm, for example. Note that using this approach it will bepossible to obtain EFOVs less than 1 μm on the Vectra, however whetherin the conventional “real FOV” approach or the modified “EFOV” approachdescribed above, the information limit of all FOVs is ultimatelydetermined by the spot size of the microscope (given sufficientstability of all other components and a suitable specimen).

It should be noted that the “Pan” described above need not be consideredas solely the use of the “Pan” or “beam Shift” of the microscope, butcould instead be a further offset applied in digital or analog spacewithin the DAC subsystem, or a raster subsystem based on one or moreDACs. The described method of rastering the beam across the field ofview in a number of discrete steps across multiple passes, ie, dwells atcyclical points A through P, as shown in FIGS. 27, 28, 30 and 31, duringsixteen image passes across the field of view, and reconstructing theresultant image from these multiple passes, whether accomplished througha “Pan” operation to move the dwell location from “A” to “B” on the nextcycle, or merely through the use of a higher resolution DAC, has theadvantage that the local current density at each of the 16 passes ismuch smaller than if the image were scanned in the traditional 1 passmethod with 16 times the dwell points.

Thus, this method can be an effective method to reduce artifacts such assample charging, drift, contamination and beam damage. One skilled inthe art will realize that the actual granularity need not be 4×4 dwellpoints (i.e. sixteen passes is not a “magic” number), and that alignmenttechniques such as are commonly employed for “drift correction” in CPBsystems may be necessary to align the “center of mass” of each imagepass to improve the overall result in the face of whatever “drift” mayoccur in the imaged area over the time it takes to acquire thesemultiple passes. Indeed, the total time to acquire 16 passes each at1/16^(th) of the number of dwell points will be very similar to the timeto acquire a single image pass in the standard manner, however in thestandard manner the drift will be distributed throughout the image(resulting in a potential “stretching” of the image features) whereas inthe method described here, for an equivalent amount of drift per unittime, this stretching will be smaller (on the order of 1/16^(th) of thestretch) per pass, and with application of drift correction betweenpasses to realign on the field of view of interest, there can be asignificant improvement in fidelity, as well as improvement due to thereduction in charging. It should be noted that the derived granularityof sub-positioning the beam may be in a “regular” fashion as describedor may be accomplished using probabilistic methods similar to thosedescribed above.

Another method to improve the scan quality when acquiring images withpixel spacings much larger than the spot size of the beam is a spatialsuper sampling method, illustrated in the flowchart of FIG. 32, which isdiscussed as follows.

The presently described method advantageously uses the pixel's intensityas a function of the average intensity of the area represents by thepixel in the image, rather than a single sampling of the area covered bythe beam itself. In order to generate this average intensity, the beamcan, during the dwell time of that pixel, be moved around randomly orsystematically within the pixel sub-area. This is known as spatialsuper-sampling.

Although from an imaging point of view this may be partiallyaccomplished by defocusing the beam to match the pixel size, defocusingmay not be suitable when patterning with gases as it affects the spotcurrent density. In the case of gas assisted etching or deposition, verylarge pixel spacings are commonly used to improve the gas efficiency,but this leads to non-uniform milling or deposition. By moving the beamaround with sub-pixel resolution during the patterning, a more uniformetch or deposition can be obtained without sacrificing the efficiency ofthe gas process. It should be noted that such movement may be in aregular fashion or using probabilistic methods similar to thosedescribed above.

As illustrated in FIG. 32, the process starts at 1102, and isimplemented when the pixel size is greater than the beam spot size 1104.The beam is positioned randomly or systematically within the pixelsub-area 1106, and is allowed to dwell at each dwell point within thepixel sub-area 1108. At the expiration of the dwell time at each pixel1110, the process continues at the next pixel in the raster 1114. At theend of the raster 1112, the spatial super-sampling process is stopped1116.

In an example embodiment, the spatial super-sampling may be implementedin a digital scan-generator by over-clocking the output DAC at a ratesignificantly higher than the dwell time. The nominal scan data can thenbe shifted by a random or fixed amount along either scan axis. Theshifted beam position is clocked out several times around each nominalpixel position. For example, by updating clocking the DAC at 50 MHz (newdata every 20 ns), it is possible to generate 50 distinct samplinglocations during a 1 μs pixel dwell time, thereby spatiallysuper-sampling the dwell area within the dwell time.

It is noted that the benefit is not limited to a case where the pixelspacing is much larger than the spot size. The benefit may also berealized by applying this technique under other conditions where thespot size is near or larger than the pixel size.

Another method to improve the scan quality when acquiring images is bytemporal sub-sampling. According to this embodiment, scan quality isimproved by extracting the signal variation in time once a dwell pointis irradiated by the beam. The intensity of a pixel is normally obtainedby summing and averaging the detector signal during the entire time thebeam is dwelling at that location. In cases where the beam shortlyinteracts with the sample, it may be useful to extract how the signalvaries in time once the sample dwell point is irradiated by the beam.This data can be used to extract dynamic process information, or toexclude one or more time slices during which there is an extraneous orotherwise undesired signal.

In an example embodiment, this can be implemented by sampling theintensity data at a higher rate than the dwell time in order to getaccess and process this data. For example, the system may sample theintensity at a frequency of 40 MHz, which produces a sample every 25 ns.Under normal circumstances, these samples are accumulated during theentire dwell period to generate an average intensity. When theinformation of interest is only in the signal after the first 200 ns,the first 8 samples might be rejected, and all subsequent samples can beaveraged, integrated, or otherwise processed to produce the displayedintensity.

By way of another example, the initial signal from an ion beam may infact contain information on the chemical state at the surface that islost after the first few ions have impacted. It may be advantageous tocombine both methods, by splitting the initial and subsequent data, orany number of time slices within the dwell period. Although processingthe intensity data in this fashion can be done in a purely analogsystem, it is easier to implement a flexible solution in a digitalsystem where the intensity is sampled by an ADC at high speed andprocessed by an FPGA or DSP prior to being displayed. This could also beaccomplished in software on a computer if the entire high speed datastream is collected and processed prior to being displayed.

The signal may be separated into different components for identifyingproperties of the sample—for example, chemical state, charge state,capacitive contrast effects, etc. based on time slices, or on thevariation of the signal within the spatial super-sampling. Also, theentire super-sampled data may be set and subdivide based oncharacteristics (rise, fall, slope, noise level, etc.) rather thanpurely on time slices.

The previously described embodiments for maneuvering the beam involvesdeflecting the beam a given amount in X and Y axes so that the beamstrikes the target at a nominally known position. One method ofaccomplishing this involves applying a voltage to a series of plates orcoils to deflect the beam in X and Y axes, with the magnitude of thevoltage correlating to the magnitude of the deflection. This particularaspect of CPB systems should be well known to those skilled in the art.Historically, this deflection was produced by analog circuitry, and thisis still the case in many systems on the market today.

More recently, systems have been marketed where the deflection positionwas determined using a digital scan generator, and a digital to analogconverter (DAC) was used to produce the deflection voltages in responseto a digital deflection code.

Many of the initial digital deflection systems and their DACs were basedon 12 bits in both X and Y axes, yielding 4,096 discrete positions thatthe beam could be deflected to, assuming sufficiently fast and stableelectronics and power supplies. These 12 bit DACs typically had thevirtue the difference in the analog output values resulting from a unitychange in the digital code applied to the DAC, say from code N to codeN+1 (effectively a change of one Least Significant Bit (LSB)) deviatesfrom the ideal difference of the analog output by no more than the idealdifference itself. Mathematically,DNL=Max(|V_(out)(i+1)−V_(out)(i))−V_(idealLSB step)), and these 12 bitDACs typically were specified such that the DNL error was less than orequal to 1 LSB, thereby guaranteeing a monotonic transfer function withno missing codes.

When observing a 20 μm field of view in such a FIB system, 12 bits weresufficient, as the 20 μm field of view (FOV) would be broken down into4,096 discrete positions, effectively mapping each position with asquare just under 0.005×0.005 μm (5 nm×5 nm) in size. As the best beamresolution achievable was on the order of 5 nm, this degree ofgranularity was sufficient for a 20 μm or smaller FOV.

When high placement resolution was required at sites outside the 20 μmFOV, it became necessary to physically move the stage in order toreposition the new site(s) within the 20 μm FOV, or to use a largerfield of view and accept the poorer placement resolution available. Forexample, to work on two sites 200 μm apart, one can either

(a) shuttle between the two sites with stage motion and continue workingwith a 20 μm field of view, 5 nm placement accuracy, and any stagepositioning error that may occur, or(b) increase the field of view to 200 μm, removing the need to move thestage and introduce a potential inaccuracy in stage motion, but insteadthe user must accept a 10 times poorer placement accuracy of just under50 nm.

Newer systems typically employ DACs and scan generators based on 16 ormore bits, to allow greater placement accuracy at larger fields of view.Another approach that will work with a 12 bit ADC is to define a fixedoffset voltage that deflects the center of the field of view a knownamount, and shuttle between points using this offset voltage rather thanstage motion, while retaining the 5 nm placement resolution.

In the CPB systems, including FIB and SEM systems, a digital to analogconverter (DAC) is used to convert a code into a corresponding voltagemagnitude for application to the system deflection plates. Given thatthe beam can be deflected in the X and Y axes, separate X and Ydeflection codes are provided by the control system. There is a range ofavailable codes spanning a min code value and a max code value, whereeach code is calibrated to provide a predetermined deflection voltage.In some systems, the scan generator and the DAC's are configured basedon 16 bit codes. Ideally, the deflection voltages from the min code tothe max code follow a linear relationship. In the presently describedCPB system, either a single DAC is used to generate both the X and Ydeflection voltages, or dedicated DAC's are used for generating the Xand Y deflection voltages. In some CPB systems, the scan generator andthe DAC's are mounted to a daughterboard, which in turn is connected toa motherboard of the system.

FIG. 33 is a partial block diagram showing a beam column 1200 having anx-axis deflector plate 1202 and a Y-axis deflector plate 1204. Y-axisdeflector plate 1204 receives a Y deflection voltage Vy, generated froman n-bit DAC 1206 in response to input code Y_CODE. Similarly, X-axisdeflector plate 1202 receives an X deflection voltage Vx, generated froman n-bit DAC 1208 in response to input code X_CODE.

Ideally, to achieve a monotonic transfer function with no missing codesat the 16 bit level, the differential non linearity (DNL) of the DAC andraster generator sub-system should not exceed 1. While the DAC may havea native DNL, additional circuitry on the daughterboard can increase thetotal DNL. It should be understood that a lower DNL is desired. DACsystems at greater than 16 bits do exist that guarantee a DNL of lessthan 1 LSB at the 16 bit level, however there is another requirement forthis application—the DAC preferably outputs at a minimum frequency onthe order of 40 MHz (25 ns dwell times). No “high speed” DACcommercially available at this point has a DNL of less than 1 across alldigital codes and can also operate at these speeds. Some high speed DACintegrated circuits do come close, and can have average DNL value whichis less than one LSB with very low standard deviation, howeverexperimental testing has discovered that large variations in the DNLoften occurs at the code boundaries which are certain powers of two,which is likely caused by the DAC architecture By example, the mostpronounced DNL variations observed in some DAC's occur at codeboundaries that are multiples of 4,096. In otherwords, certain inputcodes for the DAC will generate a voltage that is non-linear with thevoltages generated by the other codes. This is not unexpected given thearchitecture of certain DACs which are comprised of “strings” ofresistors, each responsible for a portion of the full slope of theoutput analog value; at the points where these resistor strings must bematched, it is more difficult to achieve a low DNL. It should be clearthat such variations will contribute to scan inaccuracies by the CPBsystem. For example, in the system of FIG. 33, a large non-linearvariation in Vx or Vy as the beam is being rastered across a surfacewill result in the beam being deflected in either or both the x and yaxes beyond the expected range by deflector plates 1202 and 1204, whichin the case of an image to be acquired could result in scanning a regionthat had already been scanned, or jumping ahead and missing a region ofthe sample, resulting in duplicate information or missing information inthe image. In the case where the DAC is controlling a CPB system that isremoving material, this can cause non-uniform removal rates that areundesirable.

DNL measurements for every input code of a commercially available DACdevice are shown in the graph of FIG. 34, within a range of +/−26,000 asa digital input value. On average, a DNL of magnitude 0.5 is seen foreach input code to the DAC. However, there are specific codes where theDNL spikes beyond 1, and in some cases, beyond 2. For the present DACdevice under test, these specific codes were observed to occur atmultiples of 4096 and at the central crossover of 0; these specificcodes are visible as clearly observable descending “spikes” in FIG. 34.For different DAC devices, these abnormal spikes in DNL can occur atcodes other than those seen in FIG. 34.

The effect of such abnormal DNL spikes can be mitigated by takingadvantage of the fact that output voltages corresponding to codesproximate to a code having an abnormally high DNL value, will typicallyhave low DNL values. According to the present embodiments, the outputvoltage corresponding to the target code of interest having anabnormally high DNL value is averaged with the output voltagescorresponding to codes proximate to the target code.

FIG. 35 is a block diagram of a multi-DAC voltage generator according toa present embodiment. Multi-DAC voltage generator 1300 is configured togenerate a Y deflection voltage Vy_AVG in response to three different Yinput codes. This Y deflection voltage is then provided to theY-Deflector plate of the beam column, such as deflector plate 1204 ofFIG. 33, for moving the beam in the Y axis. While not shown, anidentically configured circuit is used for generating an X deflectionvoltage Vx_AVG.

Multi-DAC voltage generator 1300 includes three identical n-bit DACdevices 1302, 1304 and 1306, and a voltage averages 1308. DAC device1304 receives the target Y input code Y_CODE generated by the rastergenerator. DAC device 1302 receives a Y input code that is one code stepabove the target Y input code, and is referred to as Y_CODE+1. A codestep is the subsequent code to a target code or the preceding code to atarget code. DAC device 1306 receives a Y input code that is one codestep below the target Y input code, and is referred to as Y_CODE−1. BothY_CODE+1 and Y_−1 can be generated automatically by the control systemin response to Y_CODE simply by incrementing Y_CODE by one code step anddecrementing Y_CODE by one code step. Accordingly, DAC 1304 generates avoltage Vy, DAC 1302 generates a voltage Vy+1 and DAC 1306 generates avoltage Vy−1. Voltage averager 1308 receives all three output voltagesand provides an output voltage Vy_AVG representing the average ofvoltages Vy, Vy+1 and Vy−1. Therefore, all three DAC devices operate inparallel, but with different input codes.

According to the principles of the presently shown embodiment, if thetarget code Y_CODE happens to have an abnormally high DNL, then theoutput voltage for DAC 1304 is averaged with the voltages provided fromthe other two DAC devices having input codes adjacent to the targetcode. Because the DNL for the other input codes adjacent to the targetcodes have normal/low DNL, the output voltages from DAC devices 1302 and1306 will have normal voltage levels expected for those codes. Thus theresulting Vy_AVG voltage for the corresponding target code becomescloser to the expected level. As previously mentioned, an identicalcircuit can be used for generating the X deflection voltage.

FIG. 36 is a circuit schematic showing an embodiment of the voltageaverager 1308 shown in FIG. 35. This simple circuit includes threeresistor elements R, each having a first terminal connected to inputslabeled V1, V2 and V3. As shown in FIG. 35, voltage averager 1308 hasinputs V1, V2 and V3 coupled to a voltage output of each of the threeDAC devices 1302, 1304 and 1306. The second terminal of each resistor Ris connected to a common output node, which is labeled as the voltageoutput V4. As shown in FIG. 35, V4 provides the averaged voltage of V1,V2 and V3 as signal Vy_AVG. If the value of each resistor R is the same,then the voltage V4 can be mathematically expressed as:

${V\; 4} = \frac{{V\; 1} + {V\; 2} + {V\; 3}}{3}$

The circuit embodiment of voltage averager 1308 shown in FIG. 36 is onepossible voltage averaging circuit which can be used, Different circuitscan be used to provide an output voltage that is an average of thereceived input voltages.

In order to illustrate the effectiveness of the presently shownembodiments for all target codes, DNL measurements for every input codefor the Multi-DAC voltage generator 1300 are shown in the graph of FIG.37. It is assumed that the same DAC devices used to measure the DNL forFIG. 34 are used in the Multi-DAC voltage generator 1300. The same graphscale is used for both FIGS. 34 and 37 for ease of comparison. Onaverage, a DNL of less than magnitude 0.5 is seen for each input code tothe DAC. When compared to the single DAC of FIG. 34, this is an overallimprovement in overall DNL. More significantly, the abnormal DNL spikesare significantly reduced relative to the spikes at the same codes shownin FIG. 34, As can be seen in FIG. 37, the maximum DNL for the abnormalspikes does not exceed 1. In contrast, the maximum DNL for the abnormalspikes in FIG. 34 all exceed 1. Accordingly, a beam system employing theMulti-DAC voltage generator embodiments shown herein benefit fromimproved raster accuracy and beam positioning. Therefore, the Multi-DACvoltage generator 1300 can be used to improve overall DNL for all inputcodes, even in the cases where all three input codes have a normal DNL,or in the cases where any one of the input codes has an abnormal DNL.

It is noted that the number of DAC's used in the embodiment of FIG. 35is not limited to 3, According to alternate embodiments, 2 DAC's can beused or more than 3 DAC's can be used, with an appropriately configuredvoltage averager 1308. In the 2 DAC alternate embodiment, two adjacentcodes can be input to the DAC's. In the more than 3 DAC alternateembodiment, multiple adjacent codes can be input to the DAC's.

According to further alternate embodiments, the codes received by theMulti-DAC voltage generator 1300, or its previously described alternateembodiments, are not limited to receiving codes that are one code apartfrom each other. More specifically, the DAC's can receive codes that aretwo or more codes apart from each other. For example in the embodimentof FIG. 35, instead of having the 3 DAC's receive Y_CODE−1, Y_CODE andY_CODE+1, they can receive Y_CODE-2, Y_CODE and Y_CODE+2. Furthermore,the codes can be asymmetric in step. For example, the 3 DAC's canreceive Y_CODE−2, Y_CODE and YCODE+1.

In the preceding description, for purposes of explanation, numerousdetails are set forth in order to provide a thorough understanding ofthe embodiments. However, it will be apparent to one skilled in the artthat these specific details are not required. In other instances,well-known electrical structures and circuits are shown in block diagramform in order not to obscure the understanding. For example, specificdetails are not provided as to whether the embodiments described hereinare implemented as a software routine, hardware circuit, firmware, or acombination thereof.

Embodiments of the disclosure can be represented as a computer programproduct stored in a machine-readable medium (also referred to as acomputer-readable medium, a processor-readable medium, or a computerusable medium having a computer-readable program code embodied therein).The machine-readable medium can be any suitable tangible, non-transitorymedium, including magnetic, optical, or electrical storage mediumincluding a diskette, compact disk read only memory (CD-ROM), memorydevice (volatile or non-volatile), or similar storage mechanism. Themachine-readable medium can contain various sets of instructions, codesequences, configuration information, or other data, which, whenexecuted, cause a processor to perform steps in a method according to anembodiment of the disclosure. Those of ordinary skill in the art willappreciate that other instructions and operations necessary to implementthe described implementations can also be stored on the machine-readablemedium. The instructions stored on the machine-readable medium can beexecuted by a processor or other suitable processing device, and caninterface with circuitry to perform the described tasks.

The above-described embodiments are intended to be examples only.Alterations, modifications and variations can be effected to theparticular embodiments by those of skill in the art without departingfrom the scope, which is defined solely by the claims appended hereto.

What is claimed is:
 1. A method for cross-sectioning a sample with apreset thickness, comprising: providing a sample having x, y and zdimensions with first and second linear fiducials each having endselectronically detectable on a first cross-section surface of the sampledefined by the x-y plane and each extending from the first cross-sectionsurface in a direction having the z-dimension component at known anglesrelative to the x-y surface; exposing a second cross-section surfacedefined by the x-y plane with a material removal tool, where an xdimension distance between ends of the first and second linear fiducialsexposed in each cross-sectioned surface changes along the z-dimension;electronically calculating a first distance in the z-dimension betweenthe first cross-section surface and the second cross-section surfacebased on a change in the x dimension distance and the angles;automatically adjusting parameters to advance the material removal toolin the z dimension for exposing a third cross-section surface at asecond distance in the z dimension from the second cross-section surfacethat is closer to the preset thickness than the first distance.
 2. Themethod of claim 1, wherein the first and the second linear fiducialseach extend from the first cross-section surface in the x-z plane. 3.The method of claim 1, wherein the at least first and second linearfiducials include grooves formed in the surface of the sample that issubstantially perpendicular to the first cross-section surface.
 4. Themethod of claim 3, wherein the at least first and second linearfiducials include protective layers formed over the grooves.
 5. Themethod of claim 1, wherein the at least first and second linearfiducials include channels extending into the sample.
 6. The method ofclaim 1, wherein the ends of the first and second linear fiducials havea predefined geometry electronically detectable on the firstcross-section surface of the sample.
 7. The method of claim 1, whereinthe at least first and second linear fiducials are formed as a firstchevron, and the angles of the first and second linear fiducials are thesame.
 8. The method of claim 7, wherein the at least first and secondlinear fiducials include a second chevron aligned with the first chevronin the x-dimension and formed behind the first chevron in thez-dimension.
 9. The method of claim 8, wherein the at least first andsecond linear fiducials further includes a pair of parallel linearfiducials extending in the z-dimension substantially perpendicular tothe first cross-section surface.
 10. The method of claim 1, whereinexposing a second cross-section surface includes operating a focused ionbeam to mill the sample from the first cross-section surface of thesample up to a distance in the z-dimension where the secondcross-section surface is exposed.
 11. The method of claim 10, whereinautomatically adjusting parameters includes adjusting a milling rate ofthe focused ion beam.
 12. The method of claim 1, wherein exposing asecond cross-section surface includes cutting the sample with anin-microscope ultramicrotome at a distance in the z-dimension where thesecond cross-section surface is exposed.
 13. The method of claim 12,wherein automatically adjusting parameters includes advancing a positionof the in-microscope ultramicrotome in the z-dimension.
 14. The methodof claim 1, further including acquiring and displaying a first image ofthe first cross-section surface at a first resolution on a display. 15.The method of claim 14, further including scanning at least one exactregion of interest in the first image defined by an arbitrary outlinepositioned on the first image.
 16. The method of claim 15, furtherincluding acquiring second image of the at least one exact region ofinterest at a second resolution greater than the first resolution, and,further including overlaying the second image of the at least one exactregion of interest at the second resolution, over the arbitrary outlinepositioned on the first image.
 17. The method of claim 16, furtherincluding scanning at the second cross-section surface, the same atleast one exact region of interest defined by the arbitrary outline fromthe image, acquiring a third image of the at least one exact region ofinterest at the second resolution, and displaying the third image in theabsence of the first image, on the display.
 18. The method of claim 14,further including scanning a first exact region of interest in the firstimage that includes at least one of the ends defined by an arbitraryoutline positioned on the first image, acquiring a second image of thefirst exact region of interest at a second resolution greater than thefirst resolution, moving a stage supporting the sample by apredetermined amount in a predetermined direction, scanning a secondexact region of interest defined by the arbitrary outline at a positionshifted by the predetermined amount in the predetermined direction,acquiring a third image of he second exact region of interest at thesecond resolution, computing a positional offset between the ends in thesecond image and the third image, and applying the positional offset toshift a beam that executes the scanning.
 19. An apparatus forcross-sectioning a sample with a preset thickness, comprising: a stagefor supporting the sample, the sample having x, y and z dimensions withfirst and second linear fiducials each having ends electronicallydetectable on a first cross-section surface of the sample defined by thex-y plane, and each extending from the first cross-section surface in adirection having the z-dimension component at known angles relative tothe x-y surface; a material removal tool configured to expose a secondcross-section surface defined by the x-y plane where an x dimensiondistance between ends of the at least first and second linear fiducialsexposed in each cross-sectioned surface changes along the z dimension;and, a computer workstation configured to calculate a first distance inthe z-dimension between the first cross-section surface and the secondcross-section surface based on a change in the x dimension distance andthe angles, and automatically adjust parameters to advance the materialremoval tool in a z-dimension for exposing a third cross-section surfaceat a second distance in the z dimension from the second cross-sectionsurface that is closer to the preset thickness than the first distance.20. The apparatus of claim 19, wherein the material removal toolincludes a focused ion beam controlled to mill the sample from the firstcross-section surface of the sample up to a distance in the z-dimensionwhere the second cross-section surface is exposed, and the computerworkstation is configured to adjust a milling rate of the focused ionbeam.
 21. The apparatus of claim 20, further including a scanningelectron microscope (SEM) configured to provide imaging data for thecomputer workstation, the computer workstation being configured tocontrol the SEM to scan the first cross-section surface for display as afirst image at a first resolution on the computer workstation, and scanat least one exact region of interest in the first image defined by anarbitrary outline positioned on the first image at a second resolutiongreater than the first resolution.
 22. The apparatus of claim 20,wherein the computer workstation is further configured to overlay thesecond image of the at least one exact region of interest at the secondresolution, over the arbitrary outline positioned on the first image.